{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的各种包\n",
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 打开数据文档\n",
    "df0,metadata =pyreadstat.read_sav(R\"data\\indentity问卷数据原始数据.sav\",apply_value_formats=True)\n",
    "# 使用自定义工具包中的函数读取spss格式文件\n",
    "df1,metadata = mytools.read_spss(R\"data\\indentity问卷数据原始数据.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 清理数据\n",
    "## 清理空白值\n",
    "### 查看所有空白值\n",
    "temp = df1[df1.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 删除指定的空白值\n",
    "\"\"\"\n",
    "## 使用dropna方法删除空值 #card\n",
    "\n",
    "1. `axis`，用于指定操作行还是列。`0`代表行，`1`代表列，默认为`0`。\n",
    "2. `how`，其值为`\"any\"`和`\"all\"`，`\"any\"`表示只要有空值，就删除。`\"all\"`表示一行或一列的所有值为空才删除。默认为`\"any\"`。\n",
    "3. `thresh`，表示保留至少含有`n`个非`na`数值的行或列。默认为`None`。\n",
    "4. `subset`，用来指定在那些列中查找缺失值。例如`df.dropna(subset=['name', 'born'])`表示删除在`'name'`和` 'born'`列含有缺失值的行。默认为`None`。\n",
    "5. `inplace`，表示是否直接在原DataFrame修改。默认为`False`，即不修改原数据框。\n",
    "\"\"\"\n",
    "df2 = df1.dropna(thresh=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 如果要删除所有空值，执行下面语句即可\n",
    "df3 = df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 删除重复值\n",
    "df4 = df3.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>问卷编号</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>民族</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>政治面貌</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年级</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您觉得自己是个典型的中国人吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>与世界其他国家的人相比中国人有自己的特点吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>做为公民，最基本的要求是爱自己的国家</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都是中华民族的一员</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>为中华民族的历史文化而骄傲</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>与中华民族的命运息息相关</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>你是否了解中华民族的传统节日</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>了解中国历史、地理、政治等</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您觉得中国怎么样</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您认为中国有多少值得自豪的地方</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您认为世界有多少比例的人尊重中国</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>对您而言作为一名中国人有多重要</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会以中国人自豪吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会隐瞒身份吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会打多少分</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国歌升起</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>世博会</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国传统文化</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>发展信心</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>你会为中国运动员呐喊助威</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>遇到灾难时中国人应该伸出援手</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>你愿意加入其他国籍吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国人要为祖国统一奋斗吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "问卷编号                    float64\n",
       "调查员                      object\n",
       "民族                     category\n",
       "政治面貌                   category\n",
       "年级                     category\n",
       "您觉得自己是个典型的中国人吗         category\n",
       "与世界其他国家的人相比中国人有自己的特点吗  category\n",
       "做为公民，最基本的要求是爱自己的国家     category\n",
       "都是中华民族的一员              category\n",
       "为中华民族的历史文化而骄傲          category\n",
       "与中华民族的命运息息相关           category\n",
       "你是否了解中华民族的传统节日         category\n",
       "了解中国历史、地理、政治等          category\n",
       "您觉得中国怎么样               category\n",
       "您认为中国有多少值得自豪的地方        category\n",
       "您认为世界有多少比例的人尊重中国       category\n",
       "对您而言作为一名中国人有多重要        category\n",
       "会以中国人自豪吗               category\n",
       "会隐瞒身份吗                 category\n",
       "会打多少分                  category\n",
       "国歌升起                   category\n",
       "世博会                    category\n",
       "中国传统文化                 category\n",
       "发展信心                   category\n",
       "你会为中国运动员呐喊助威           category\n",
       "遇到灾难时中国人应该伸出援手         category\n",
       "你愿意加入其他国籍吗             category\n",
       "中国人要为祖国统一奋斗吗           category"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 检查数据类型\n",
    "\n",
    "### 查看所有变量的数据类型\n",
    "df4.dtypes.to_frame()\n",
    "### 使用dtypes属性可以查看所有变量或指定变量的类型。\n",
    "### 使用to_frame方法的目的在于输出更加整洁的文本，非必须。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "      <th>问卷编号</th>\n",
       "      <td>int32</td>\n",
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       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
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       "    <tr>\n",
       "      <th>民族</th>\n",
       "      <td>category</td>\n",
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       "    <tr>\n",
       "      <th>政治面貌</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年级</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您觉得自己是个典型的中国人吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>与世界其他国家的人相比中国人有自己的特点吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>做为公民，最基本的要求是爱自己的国家</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都是中华民族的一员</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>为中华民族的历史文化而骄傲</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>与中华民族的命运息息相关</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>你是否了解中华民族的传统节日</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>了解中国历史、地理、政治等</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您觉得中国怎么样</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您认为中国有多少值得自豪的地方</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>您认为世界有多少比例的人尊重中国</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>对您而言作为一名中国人有多重要</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会以中国人自豪吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会隐瞒身份吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会打多少分</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国歌升起</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>世博会</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国传统文化</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>发展信心</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>你会为中国运动员呐喊助威</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>遇到灾难时中国人应该伸出援手</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>你愿意加入其他国籍吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国人要为祖国统一奋斗吗</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "问卷编号                      int32\n",
       "调查员                      object\n",
       "民族                     category\n",
       "政治面貌                   category\n",
       "年级                     category\n",
       "您觉得自己是个典型的中国人吗         category\n",
       "与世界其他国家的人相比中国人有自己的特点吗  category\n",
       "做为公民，最基本的要求是爱自己的国家     category\n",
       "都是中华民族的一员              category\n",
       "为中华民族的历史文化而骄傲          category\n",
       "与中华民族的命运息息相关           category\n",
       "你是否了解中华民族的传统节日         category\n",
       "了解中国历史、地理、政治等          category\n",
       "您觉得中国怎么样               category\n",
       "您认为中国有多少值得自豪的地方        category\n",
       "您认为世界有多少比例的人尊重中国       category\n",
       "对您而言作为一名中国人有多重要        category\n",
       "会以中国人自豪吗               category\n",
       "会隐瞒身份吗                 category\n",
       "会打多少分                  category\n",
       "国歌升起                   category\n",
       "世博会                    category\n",
       "中国传统文化                 category\n",
       "发展信心                   category\n",
       "你会为中国运动员呐喊助威           category\n",
       "遇到灾难时中国人应该伸出援手         category\n",
       "你愿意加入其他国籍吗             category\n",
       "中国人要为祖国统一奋斗吗           category"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 使用astypes方法设定变量的类型\n",
    "df5 = df4.astype({'问卷编号':'int'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "很有信心     486\n",
       "担忧不好说    279\n",
       "不清楚       54\n",
       "没信心       40\n",
       "Name: 发展信心, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "异常值清理\n",
    "\n",
    "类别变量异常值的查看及修改\n",
    "\n",
    "使用value_counts方法可以查看类别变量的取值及次数\n",
    "\"\"\"\n",
    "df5['发展信心'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "汉族      363\n",
      "回族      131\n",
      "藏族       50\n",
      "蒙古族      50\n",
      "壮族       47\n",
      "土家族      46\n",
      "维吾尔族     45\n",
      "苗族       34\n",
      "彝族       15\n",
      "白族       15\n",
      "满族       11\n",
      "哈萨克族     10\n",
      "瑶族        8\n",
      "布依族       7\n",
      "侗族        5\n",
      "东乡族       4\n",
      "黎族        3\n",
      "哈尼族       3\n",
      "水族        2\n",
      "仫佬族       2\n",
      "羌族        2\n",
      "畲族        1\n",
      "土族        1\n",
      "其他        1\n",
      "保安族       1\n",
      "裕固族       1\n",
      "毛难族       1\n",
      "Name: 民族, dtype: int64\n",
      "团员    631\n",
      "党员    112\n",
      "群众     95\n",
      "其他     21\n",
      "Name: 政治面貌, dtype: int64\n",
      "大一      306\n",
      "大二      274\n",
      "大三      170\n",
      "大四       94\n",
      "预科       14\n",
      "22.0      1\n",
      "Name: 年级, dtype: int64\n",
      "完全是     391\n",
      "可以算     249\n",
      "一般般     137\n",
      "不太算      56\n",
      "完全不是     26\n",
      "Name: 您觉得自己是个典型的中国人吗, dtype: int64\n",
      "较有特点     354\n",
      "非常有特点    321\n",
      "一般般      126\n",
      "较无特点      34\n",
      "毫无特点      24\n",
      "Name: 与世界其他国家的人相比中国人有自己的特点吗, dtype: int64\n",
      "完全同意     358\n",
      "同意       346\n",
      "有点同意     108\n",
      "不同意       29\n",
      "完全不同意     18\n",
      "Name: 做为公民，最基本的要求是爱自己的国家, dtype: int64\n",
      "完全同意     458\n",
      "同意       279\n",
      "有点同意      83\n",
      "不同意       22\n",
      "完全不同意     17\n",
      "Name: 都是中华民族的一员, dtype: int64\n",
      "同意       339\n",
      "完全同意     230\n",
      "有点同意     200\n",
      "不同意       73\n",
      "完全不同意     17\n",
      "Name: 为中华民族的历史文化而骄傲, dtype: int64\n",
      "同意       336\n",
      "完全同意     270\n",
      "有点同意     165\n",
      "不同意       66\n",
      "完全不同意     22\n",
      "Name: 与中华民族的命运息息相关, dtype: int64\n",
      "比较了解     518\n",
      "完全了解     203\n",
      "不太了解     122\n",
      "完全不了解     16\n",
      "Name: 你是否了解中华民族的传统节日, dtype: int64\n",
      "比较了解     526\n",
      "完全了解     170\n",
      "不太了解     149\n",
      "完全不了解     14\n",
      "Name: 了解中国历史、地理、政治等, dtype: int64\n",
      "挺好     472\n",
      "一般般    194\n",
      "十分棒    140\n",
      "较差      38\n",
      "很差劲     15\n",
      "Name: 您觉得中国怎么样, dtype: int64\n",
      "很多     396\n",
      "特别多    194\n",
      "有一些    193\n",
      "很少      62\n",
      "特别少     14\n",
      "Name: 您认为中国有多少值得自豪的地方, dtype: int64\n",
      "较多比例     408\n",
      "不多的比例    233\n",
      "大多数比例    173\n",
      "极少比例      45\n",
      "Name: 您认为世界有多少比例的人尊重中国, dtype: int64\n",
      "比较重要    342\n",
      "十分重要    305\n",
      "一般般     160\n",
      "不太重要     42\n",
      "毫不重要     10\n",
      "Name: 对您而言作为一名中国人有多重要, dtype: int64\n",
      "会      412\n",
      "有些     247\n",
      "无所谓    126\n",
      "不太会     53\n",
      "不会      21\n",
      "Name: 会以中国人自豪吗, dtype: int64\n",
      "绝对不会       485\n",
      "可能会        124\n",
      "也许会也许不会    114\n",
      "很可能不会       76\n",
      "一定会         60\n",
      "Name: 会隐瞒身份吗, dtype: int64\n",
      "六十到八十    411\n",
      "八十到一百    237\n",
      "四十到六十    157\n",
      "20~40     28\n",
      "零到二十      26\n",
      "Name: 会打多少分, dtype: int64\n",
      "比较激动      347\n",
      "感到非常激动    255\n",
      "一般        200\n",
      "没感觉        56\n",
      "5.0         1\n",
      "Name: 国歌升起, dtype: int64\n",
      "感到自豪    472\n",
      "比较好     235\n",
      "一般      128\n",
      "没感觉      23\n",
      "5.0       1\n",
      "Name: 世博会, dtype: int64\n",
      "富有内涵，意义深刻    612\n",
      "比较肯定         221\n",
      "古板，跟不上潮流      26\n",
      "Name: 中国传统文化, dtype: int64\n",
      "很有信心     486\n",
      "担忧不好说    279\n",
      "不清楚       54\n",
      "没信心       40\n",
      "Name: 发展信心, dtype: int64\n",
      "肯定会        482\n",
      "大多数情况下会    279\n",
      "不一定         77\n",
      "绝对不会        21\n",
      "Name: 你会为中国运动员呐喊助威, dtype: int64\n",
      "很赞同     375\n",
      "赞同      341\n",
      "不一定     105\n",
      "不赞同      23\n",
      "很不赞同     15\n",
      "Name: 遇到灾难时中国人应该伸出援手, dtype: int64\n",
      "不加入       314\n",
      "其他        251\n",
      "做外籍华人     247\n",
      "毫不犹豫加入     47\n",
      "Name: 你愿意加入其他国籍吗, dtype: int64\n",
      "赞同      389\n",
      "很赞同     326\n",
      "不一定     100\n",
      "不赞同      32\n",
      "很不赞同     11\n",
      "55.0      1\n",
      "Name: 中国人要为祖国统一奋斗吗, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 列出所有类别变量 \"\"\"\n",
    "for col in df5.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "汉族      363\n",
      "回族      131\n",
      "藏族       50\n",
      "蒙古族      50\n",
      "壮族       47\n",
      "土家族      46\n",
      "维吾尔族     45\n",
      "苗族       34\n",
      "彝族       15\n",
      "白族       15\n",
      "满族       11\n",
      "哈萨克族     10\n",
      "瑶族        8\n",
      "布依族       7\n",
      "侗族        5\n",
      "东乡族       4\n",
      "黎族        3\n",
      "哈尼族       3\n",
      "水族        2\n",
      "仫佬族       2\n",
      "羌族        2\n",
      "畲族        1\n",
      "土族        1\n",
      "其他        1\n",
      "保安族       1\n",
      "裕固族       1\n",
      "毛难族       1\n",
      "Name: 民族, dtype: int64\n",
      "团员    631\n",
      "党员    112\n",
      "群众     95\n",
      "其他     21\n",
      "Name: 政治面貌, dtype: int64\n",
      "大一      306\n",
      "大二      274\n",
      "大三      170\n",
      "大四       94\n",
      "预科       14\n",
      "22.0      1\n",
      "Name: 年级, dtype: int64\n",
      "完全是     391\n",
      "可以算     249\n",
      "一般般     137\n",
      "不太算      56\n",
      "完全不是     26\n",
      "Name: 您觉得自己是个典型的中国人吗, dtype: int64\n",
      "较有特点     354\n",
      "非常有特点    321\n",
      "一般般      126\n",
      "较无特点      34\n",
      "毫无特点      24\n",
      "Name: 与世界其他国家的人相比中国人有自己的特点吗, dtype: int64\n",
      "完全同意     358\n",
      "同意       346\n",
      "有点同意     108\n",
      "不同意       29\n",
      "完全不同意     18\n",
      "Name: 做为公民，最基本的要求是爱自己的国家, dtype: int64\n",
      "完全同意     458\n",
      "同意       279\n",
      "有点同意      83\n",
      "不同意       22\n",
      "完全不同意     17\n",
      "Name: 都是中华民族的一员, dtype: int64\n",
      "同意       339\n",
      "完全同意     230\n",
      "有点同意     200\n",
      "不同意       73\n",
      "完全不同意     17\n",
      "Name: 为中华民族的历史文化而骄傲, dtype: int64\n",
      "同意       336\n",
      "完全同意     270\n",
      "有点同意     165\n",
      "不同意       66\n",
      "完全不同意     22\n",
      "Name: 与中华民族的命运息息相关, dtype: int64\n",
      "比较了解     518\n",
      "完全了解     203\n",
      "不太了解     122\n",
      "完全不了解     16\n",
      "Name: 你是否了解中华民族的传统节日, dtype: int64\n",
      "比较了解     526\n",
      "完全了解     170\n",
      "不太了解     149\n",
      "完全不了解     14\n",
      "Name: 了解中国历史、地理、政治等, dtype: int64\n",
      "挺好     472\n",
      "一般般    194\n",
      "十分棒    140\n",
      "较差      38\n",
      "很差劲     15\n",
      "Name: 您觉得中国怎么样, dtype: int64\n",
      "很多     396\n",
      "特别多    194\n",
      "有一些    193\n",
      "很少      62\n",
      "特别少     14\n",
      "Name: 您认为中国有多少值得自豪的地方, dtype: int64\n",
      "较多比例     408\n",
      "不多的比例    233\n",
      "大多数比例    173\n",
      "极少比例      45\n",
      "Name: 您认为世界有多少比例的人尊重中国, dtype: int64\n",
      "比较重要    342\n",
      "十分重要    305\n",
      "一般般     160\n",
      "不太重要     42\n",
      "毫不重要     10\n",
      "Name: 对您而言作为一名中国人有多重要, dtype: int64\n",
      "会      412\n",
      "有些     247\n",
      "无所谓    126\n",
      "不太会     53\n",
      "不会      21\n",
      "Name: 会以中国人自豪吗, dtype: int64\n",
      "绝对不会       485\n",
      "可能会        124\n",
      "也许会也许不会    114\n",
      "很可能不会       76\n",
      "一定会         60\n",
      "Name: 会隐瞒身份吗, dtype: int64\n",
      "六十到八十    411\n",
      "八十到一百    237\n",
      "四十到六十    157\n",
      "20~40     28\n",
      "零到二十      26\n",
      "Name: 会打多少分, dtype: int64\n",
      "比较激动      347\n",
      "感到非常激动    255\n",
      "一般        200\n",
      "没感觉        56\n",
      "5.0         1\n",
      "Name: 国歌升起, dtype: int64\n",
      "感到自豪    472\n",
      "比较好     235\n",
      "一般      128\n",
      "没感觉      23\n",
      "5.0       1\n",
      "Name: 世博会, dtype: int64\n",
      "富有内涵，意义深刻    612\n",
      "比较肯定         221\n",
      "古板，跟不上潮流      26\n",
      "Name: 中国传统文化, dtype: int64\n",
      "很有信心     486\n",
      "担忧不好说    279\n",
      "不清楚       54\n",
      "没信心       40\n",
      "Name: 发展信心, dtype: int64\n",
      "肯定会        482\n",
      "大多数情况下会    279\n",
      "不一定         77\n",
      "绝对不会        21\n",
      "Name: 你会为中国运动员呐喊助威, dtype: int64\n",
      "很赞同     375\n",
      "赞同      341\n",
      "不一定     105\n",
      "不赞同      23\n",
      "很不赞同     15\n",
      "Name: 遇到灾难时中国人应该伸出援手, dtype: int64\n",
      "不加入       314\n",
      "其他        251\n",
      "做外籍华人     247\n",
      "毫不犹豫加入     47\n",
      "Name: 你愿意加入其他国籍吗, dtype: int64\n",
      "赞同      389\n",
      "很赞同     326\n",
      "不一定     100\n",
      "不赞同      32\n",
      "很不赞同     11\n",
      "55.0      1\n",
      "Name: 中国人要为祖国统一奋斗吗, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "### 使用自定义函数打印初全部类别变量取值\n",
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('年级==22')\n",
    "df5.loc[749,'年级'] = '大二'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('国歌升起==5')\n",
    "df5.loc[409,'国歌升起'] = '感到非常激动'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('世博会==5')\n",
    "df5.loc[158,'世博会'] = '感到自豪'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('中国人要为祖国统一奋斗吗==55')\n",
    "df5.loc[188,'中国人要为祖国统一奋斗吗'] = '很赞同'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    859.000000\n",
       "mean     455.071013\n",
       "std      259.316464\n",
       "min        1.000000\n",
       "25%      231.500000\n",
       "50%      460.000000\n",
       "75%      679.500000\n",
       "max      900.000000\n",
       "Name: 问卷编号, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看数值变量的异常值\n",
    "### 使用describe方法对数值变量进行描述统计，可得到最小值、最大值、方差等信息\n",
    "df5['问卷编号'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    859.000000\n",
      "mean     455.071013\n",
      "std      259.316464\n",
      "min        1.000000\n",
      "25%      231.500000\n",
      "50%      460.000000\n",
      "75%      679.500000\n",
      "max      900.000000\n",
      "Name: 问卷编号, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "### 使用自定义函数对不同类型的数值变量进行描述统计\n",
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 逻辑一致性清理\n",
    "### 构造逻辑判断表达式\n",
    "c1 = '会以中国人自豪吗 == \"会\" and 会隐瞒身份吗 == \"一定会\"'\n",
    "### 筛选出满足条件的数据\n",
    "temp = df5.query(c1)[['会以中国人自豪吗','会隐瞒身份吗']]\n",
    "### 删除满足上述条件的数据\n",
    "df6 = df5.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2 = '对您而言作为一名中国人有多重要 == \"十分重要\" and 你愿意加入其他国籍吗 == \"毫不犹豫加入\"'\n",
    "temp = df6.query(c2)[['对您而言作为一名中国人有多重要','你愿意加入其他国籍吗']]\n",
    "### 删除满足上述条件的数据\n",
    "df7 = df6.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据清理完毕\n",
    "df = df7.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述统计\n",
    "## 单变量描述统计\n",
    "### 无序类别变量（定类变量）描述统计，\n",
    "### 可使用频数频率表、众数、柱状图等方式进行描述\n",
    "result = df['政治面貌'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>政治面貌</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>团员</td>\n",
       "      <td>605</td>\n",
       "      <td>73.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>党员</td>\n",
       "      <td>106</td>\n",
       "      <td>12.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>群众</td>\n",
       "      <td>94</td>\n",
       "      <td>11.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>其他</td>\n",
       "      <td>18</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>823</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  政治面貌   个数     百分比\n",
       "0   团员  605   73.51\n",
       "1   党员  106   12.88\n",
       "2   群众   94   11.42\n",
       "3   其他   18    2.19\n",
       "4   总和  823  100.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['政治面貌'] = df['政治面貌'].value_counts().index\n",
    "b['个数'] = df['政治面貌'].value_counts().values\n",
    "b['百分比'] = df['政治面貌'].value_counts(normalize=True).values * 100\n",
    "# b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'政治面貌':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "temp = pd.concat([b,total_row],ignore_index=True)\n",
    "temp['百分比'] = temp['百分比'].apply(lambda x: round(x, 2))\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>政治面貌</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>团员</td>\n",
       "      <td>605</td>\n",
       "      <td>73.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>党员</td>\n",
       "      <td>106</td>\n",
       "      <td>12.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>群众</td>\n",
       "      <td>94</td>\n",
       "      <td>11.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>其他</td>\n",
       "      <td>18</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>823</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  政治面貌   个数     百分比\n",
       "0   团员  605   73.51\n",
       "1   党员  106   12.88\n",
       "2   群众   94   11.42\n",
       "3   其他   18    2.19\n",
       "4   总和  823  100.00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'政治面貌')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>民族</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>汉族</td>\n",
       "      <td>342</td>\n",
       "      <td>41.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>回族</td>\n",
       "      <td>127</td>\n",
       "      <td>15.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>藏族</td>\n",
       "      <td>49</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>蒙古族</td>\n",
       "      <td>49</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>壮族</td>\n",
       "      <td>47</td>\n",
       "      <td>5.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>土家族</td>\n",
       "      <td>43</td>\n",
       "      <td>5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>维吾尔族</td>\n",
       "      <td>42</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>苗族</td>\n",
       "      <td>32</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>彝族</td>\n",
       "      <td>15</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>白族</td>\n",
       "      <td>15</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>满族</td>\n",
       "      <td>11</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>哈萨克族</td>\n",
       "      <td>10</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>瑶族</td>\n",
       "      <td>8</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>布依族</td>\n",
       "      <td>7</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>侗族</td>\n",
       "      <td>5</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>东乡族</td>\n",
       "      <td>4</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>黎族</td>\n",
       "      <td>3</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>水族</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>仫佬族</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>羌族</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>哈尼族</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>畲族</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>土族</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>其他</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>保安族</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>裕固族</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>毛难族</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>总和</td>\n",
       "      <td>823</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      民族   个数     百分比\n",
       "0     汉族  342   41.56\n",
       "1     回族  127   15.43\n",
       "2     藏族   49    5.95\n",
       "3    蒙古族   49    5.95\n",
       "4     壮族   47    5.71\n",
       "5    土家族   43    5.22\n",
       "6   维吾尔族   42    5.10\n",
       "7     苗族   32    3.89\n",
       "8     彝族   15    1.82\n",
       "9     白族   15    1.82\n",
       "10    满族   11    1.34\n",
       "11  哈萨克族   10    1.22\n",
       "12    瑶族    8    0.97\n",
       "13   布依族    7    0.85\n",
       "14    侗族    5    0.61\n",
       "15   东乡族    4    0.49\n",
       "16    黎族    3    0.36\n",
       "17    水族    2    0.24\n",
       "18   仫佬族    2    0.24\n",
       "19    羌族    2    0.24\n",
       "20   哈尼族    2    0.24\n",
       "21    畲族    1    0.12\n",
       "22    土族    1    0.12\n",
       "23    其他    1    0.12\n",
       "24   保安族    1    0.12\n",
       "25   裕固族    1    0.12\n",
       "26   毛难族    1    0.12\n",
       "27    总和  823  100.00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'民族')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 调用外部自定义函数绘制柱状图\n",
    "mytools.show_bar(df,'政治面貌')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.variable_value_labels['年级'].values()\n",
    "df = mytools.set_ordered_by_meta(metadata,df,'年级')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年级</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比（%）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>预科</td>\n",
       "      <td>14</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>大一</td>\n",
       "      <td>285</td>\n",
       "      <td>34.63</td>\n",
       "      <td>36.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>大二</td>\n",
       "      <td>264</td>\n",
       "      <td>32.08</td>\n",
       "      <td>68.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大三</td>\n",
       "      <td>167</td>\n",
       "      <td>20.29</td>\n",
       "      <td>88.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>大四</td>\n",
       "      <td>93</td>\n",
       "      <td>11.30</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>总和</td>\n",
       "      <td>823</td>\n",
       "      <td>100.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   年级   个数     百分比  累计百分比（%）\n",
       "0  预科   14    1.70      1.70\n",
       "1  大一  285   34.63     36.33\n",
       "2  大二  264   32.08     68.41\n",
       "3  大三  167   20.29     88.70\n",
       "4  大四   93   11.30    100.00\n",
       "5  总和  823  100.00       NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 单变量描述统计\n",
    "### 有序类别变量（定序变量）描述统计，\n",
    "### 可使用频数频率表（累计频率）、众数、柱状图等方式进行描述\n",
    "from pandas.api.types import CategoricalDtype\n",
    "# 设定有序变量\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['预科','大一', '大二','大三', '大四'], ordered=True)\n",
    "df = df.astype({'年级':cat_dtype})\n",
    "mytools.ordinal_desc(df,'年级')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'年级',sort=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    823.000000\n",
       "mean       7.561361\n",
       "std        2.736313\n",
       "min        0.000000\n",
       "25%        6.000000\n",
       "50%        8.000000\n",
       "75%       10.000000\n",
       "max       12.000000\n",
       "Name: 认知维度, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 单变量描述统计\n",
    "### 数值变量描述统计，\n",
    "### 可使用直方图、平均值、极差、四分位值等方式进行描述\n",
    "df['认知维度']= df['您觉得自己是个典型的中国人吗'].cat.codes + df['与世界其他国家的人相比中国人有自己的特点吗'].cat.codes + df['做为公民，最基本的要求是爱自己的国家'].cat.codes\n",
    "df['认知维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='认知维度', ylabel='Count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WeYxUlp+hGSXZmlWaowyfNyo1ID6s2d2knz/r3P56xrhsfemc2S5VdPg8xmjZlEI9/taBpV+1rd3a2dChKUVZLlaGWEEoAwAAACSButZuvV3dog1VrUcUxKSleOSJeIe/2x8Y0QyboJX2NHZqT2Onnt+8T1NLsnTUhFxNLsyUJ8560mB0dfYEdMODaxTo9xcqxWP0kw8eo/Q4De9mjc/RK9sb1Nx5YLbMih0NhDKQRCgDAAAAJKxA0Gpzbate39Wk2tbuQ74vP8O3vxdMYVaqslJThm3ka61Vlz+o9m6/OnoCau7sVV1rt+rCfWn8wyQ2AWu1pbZNW2rblJXm1dEV+VpYnqe0OP0FHEfm1ic2altdu2Psf86YqQUVeS5VdOQ8HqOlUwr01Nu1+8f2NnVpX1u3irPTXKwMsYBQBgAAAEg0vnSt3tWoNbub1NrlP+jlBZk+TSnK0uSiTJXmpSstZWSBiDFGGT7voMuQgkGrho4e7arv0Pb6du1t6hxyVk17d0Avb63Xqh2Nml+eq2MnFSg7jV9ZksXKHQ2688XtjrGFE/P1mdOnu1TR6JlTmquXttaro+fALLX1lc06fTbbsSc7fsIBAAAAicKXrryTL1fekgv0/OZ9w15ampuuOaU5mlqcpdwM35iV5PEYFWenqTg7TYsmF6jHH9Suhg5tqmnVtrp2BezAhKYnENTqXU1au7tZcyfk6LipRcpO51eXRNbVG9AX/7pO/f86pKV49KOLFyrFG/9Nob0eo3lluVqxo3H/2IaqVp08o1i+BPj+cPj4yQYAAADEua7egO59eae8F/yf8tOH3mo3Oy1Fc0pzdNSEXNe2FU5N8WjGuGzNGJetrt6ANla36q2qlkGXVwWs1Rt7W7ShulWLJhVo0eT8Ec/iQXz40RMbtX2fc9nSF941WzPGJc7W0fPL8hyhTE8gqI01rZpfFr9Ls3DkCGUAAACAOBUIWv111W799KnNqmrukhkikCnNTdfiyQWaVpI1oEmvm9J9Xi2cmK+FE/NV09KlVTsbtaW2TZFzZ/xBq9d2NGh9ZbOOm1ao+WV5g/a2QXxatbNBd7zgXLZ07KR8XXPyVJcqGhu5GT5NKcrUjvqO/WNvVDYTyiS5pJsnZYz5lTHGGmOmHOS68eFrdxtjuo0xG4wx1xtjhv0zM8YsMsb83RhTb4zpNMa8ZIx596h+EwAAAEh6q3Y26L0/f0Ff/tt6VTV3DXrN9JIsXby4QpcsnagZ47JjKpCJND43XecumKCrTpis+eW5g4Yunb0BPbexTve/tktVzZ0uVInRNtiypdQUj3540dEJGbwtKHcGMDUt3aptGfz/XySHpJopY4z5sqRPHsJ1kyW9IKkiPGQlzZb0E0nHSrp6iPveLemfknz97jtB0iPGmGuttb8/om8AAAAASa+utVvff2yD/rZ6z5DX9OxYrWsueZ+K4nBnl/zMVJ05Z7yOn1qkV7bX6829LYpsO1Pf3qMHV+7RgvI8nTS9iJ2a4thPntw0YLelG86apRnjclyqaGxNKcpSdlqK2roPNOBeX9msM3PTXawKbkqamTLGmBsk3XwI1/kkPapQIFMv6X0KhSxzJK2RdJUx5pJB7psh6a/ha1+XdIxCodfZkpok/cwYk1jz7wAAABA1gaDV3S9u1xm3PjdkIBOs3qiqe29Ux9O/iMtApr+stBSdOWe8rjhusqYVZw16zfrKZt3zyk5tqmmVHaRhMGLbqp2N+t3z2xxjCyfm62MJtmypP0+44W9/G2ta1eMPulQR3JbwoYwxJtMYc7+kH0ka+u2EAz4u6SiFZrm831r7T2ttwFq7UdKFkvySfmqMiYzjb5aUKalG0lnW2rXW2qC19glJnwufu2V0visAAAAkky21rbr41y/pW/9+S63dA7e4njkuW3+4ZpmCT/1YPXs3ulDh2CnMStX5C8t04aJyFWcPbE7c0RPQY29U69E3qtXZGxjkGRCLunoD+uJf1jq2R0/1enTrRUcnxG5Lw5lflqf+Kwl7A1YbqlvcKwiuSuy/7SHfknSppFclLTuE6/uWNz1srX2+/wlr7TZJf5dUKunEvnFjzDhJHwg//IG1tj7iOe+XtFfSecYY5qUBAADgkPQGgvr5M5t17m0vaPWupgHns9NS9PXz5urRz52i02aVRL/AKKooyNRlSyfplBnFShmk18iW2jb96ZWd2lHfPsjdiDU/emKjtkXstnT9WTM1c3xiLlvqLzs9RVOLnLO/1lc2M9srSSVDKBOU9A1JJ1trq4e70BhTIGl++OGDQ1z2aPjYv3nvSTrwZ/mXyBustX5JTyo0W+bUQysbAAAAyezNvc06//YXdOsTm9QTGLi04QPHluuZG0/Tx06ZJl+Czyzo4/EYLZpcoCuPn6wpRZkDzrf3BPTPNXv17IZa9Q7yZ4bYsHLHwN2WFk7M18dPmeZSRdG3oMLZ8HdfW49qWgZuC4/ElwyNfr9hre09xGtn9Pv85SGuWRc+zhnkvj3W2t2HcN8Th1iPjDEVB7mk9FCfCwAAALHPHwjqN8u36adPbVJvYOA751OKMnXzB47WCdOLXKguNuRm+PTehWXaXNumZzfUqiuiH8e6ymbtbuzQuQsmqDjOe+skms6ewXdb+tHFib9sqb/JhZnKTU9RS9eB5YhvV7WoNI+FFckm4UOZEQQyktT3L1uvpB1DXFMbPk4Z5L5Nwzz3YPcdiqFCHgAAACSYHfvadcODawZdquQx0rWnTNP175yljFR2GzLGaNb4HJXnZ+jJt2u0s77Dcb6xo1cPrNit02eP01ERjVXhnluf2KjtEcuWbkzg3ZaGYozRUWW5emVbw/6xzbVtOm1WiTwJuBU4hpY8UeSh6YvRW6y1Q3UJawwfJwxyX6OGNth9AAAAgKy1+uMrO/Xu254fNJCZU5qjf3zmJH313LkEMhGy0lJ0wcIynT67ZECvGX/Q6sm3a/TEW9UsZ4oBr21v0J0vOpctHTspXx9LomVL/c2O6J/T2RvQ7saOIa5Gokr4mTIj1BfEDPd/Qk/4mDEK9x2KiQc5XyppxQifEwAAADGiob1HX/7bOj35Vs2Acx4jffodM/TZM2cqNYX3U4dijNHRFfmaWJip/7xRrdpWZ2+Ot6taVdvSrfOOnqCCzIE7OGHsdfT49aW/rh2wbOmHFy2UN0lnhuRnpmpcTprj7+vGmlZNLhp8C3gkJkIZp87wsWeYa/oi9v6dxQ73voOy1g67jbcxyfkDDAAAIBG8tGWfPv/gmkEbfE4pytSPPniMFk8ucKGy+FSQmaqLF1fo+c37tK6y2XGuvr1Hf35tt86eP17TirNdqjB5/eA/G7UjYonZF981WzPGJfd/i9mlOY5QZmttu/yzg0nVXyfZ8V/aqW+JUdkw1/T9q9g/Dem7r3yE9wEAACAJ9QaCuuU/G3T5718dNJC54vhJevRzpxDIHIYUr0enzxmnd88vVWrEL7Y9gaD+vbZKr21vEC/Lo+eVbfW6+6UdjrHFkwt0zclT3SkohsyK6KXTEwgOCK+Q2AhlnHaFjxnGmKF2NRofPrYNct9wP1UGuw8AAABJZmd9uy761Uv61XNbHUs5JKkoK1V3fniJvve+BcpMZVL7kZg1PkeXLpuo4uyBy5Ve3lav4gu+IpM60s4CGKnQsqV1jrG0FI9+eNHRSbtsqb/s9BSV5zv/Hm6qaXWpGriBUKYfa+0+HQhYlgxx2fHh465+Y6vCx+nGmPwR3AcAAIAk8tDqPTr3tue1dk/zgHOnzCzWY9efojPmjB/kThyOgsxUfXDJRM0aP3CJTOasE1R6xa3y5pa4UFnyuOWxDdrVELFs6ezZmlaS3MuW+ots+LttX7t6/DSmThaEMgM9GT5eOsT5s8LH1X0D1trtkrYq1KPnosgbjDFeSWdE3gcAAHA4gtaqubNX9W3djo/Gjh752WEmJrV29er6P7+uGx5cq/Ye5yafPq/R18+bqz98ZJnG5aS7VGHi8nk9OmdeqU6eUTxgwVJqyWSNu+R7qmruHPReHJmXt9brDy/vdIwtnVKgj5zEsqX+ZozPVv9JQ4Gg1bY6FlgkC+ZEDnS3pI9KusQY82Nr7f4QxRgzR9L7ww8fHuS+70r6ujHmAWtt/zlnVyvUp8Yv6fExqhsAACSgQNBqT2OHqpu71NDRo4b2HjV29CoQtEPek5OeovwMn/IzU1WUnapJBZnKz/SxQYBLVu1s1PUPvK7dDQN/8Z9WnKWfXXas5pfnuVBZ8jDGaPHkAhVnp+qxN6rV3W8WgjcrX39bXamz5o7X7NKcYZ4FI9Ha1asv/nWtYyzdl9y7LQ0lw+fVpMJMRy+ZjTWtmjMh18WqEC2EMhGstS8YY16UdJKkR4wxH5X0tKQTJd0pKVWhWTH/irj1N5JulDRZ0hPGmE+Er7tU0s/D19xnra0e++8CAADEs2DQandjhzbXtmlrbZu6RjiNvbXLr9Yuv3Y3HggBstNSNKkwUxMLMzSlKEvpPu9ol40I/kBQP392i25/ZsugIdolSybqm+cfpaw0XpJHy+SiLF22bJL+tXavGtoPbJwaCFr9581qNXX0aNnUQgLMUfDNf76pPY3OIPLL58zRlGK2ex7M7PE5jlBmV0OHOnsDyuBndcLjX4DBXS5puaRJkh6JONcl6WprbW//QWttnTHmQ5IeUqh/zNqI+7ZL+sLYlAsAABJBtz+g1TubtL6yWZ29gYPfMAJt3X69VdWit6pa5DVGU4ozNXdCrqYUZfGu9RjYVd+h6x94Xat3NQ04l5Oeops/sEDvOXq4DT8xVvIyfPrgkgr95O6/KmPqIse5V7Y3qKmzV2fOHacUD50eDtc/Xq/U31+vdIwtm1qoq0+Y4k5BcWBaSba8ntr9AW7QSltq2rSggll0iS7pftJYa034Y8cw1+xUqNHv3ZJ6+p16TdKp1toXh7jvMYUCmef6DQcl/UXSCdbauiMqHgAAJKTeQFArdzborhd36LUdDaMeyEQKWKutde16eF2V7nhhm57dWOuYNYDDZ63V31bt0bk/e37QQGbplAI99rlTCGRclpbiVd3fvqPW1ZHvv0obqlv10OpKdfT4Xags/u2q79DX//GGYywnPUU//uBCeQiAh5Sa4tHUiFlEG9mFKSkwU2YI4QDlI8aY6yXNlFQXDmsOdt9aSacbY8oklUvaZq2tH9NiAQBAfDIepc4+TX94aceA5q+RxuemaUJuhgqzUlWYlaqCLN+Aae09gaCaO3vV1NGrps5eNbT3aE9jh9q7h37urt6g1u1p1ro9zZpcmKljJuVrcmEmyzcOQ3NHr/73H+v18LqqAee8HqPPv3OmPvWOGcxMihU2qIYnf6VAS50K3vFh9V9gVtXcpQdX7tF7F5apMGvgltoYnD8Q1PUPvK62bmeg9f/ev0AVBZkuVRU/Zo/P0ZbaAw1+K5s61dHjV2Yqv7YnMv7rHoS1tlnSysO4b6+kvaNfEQAASAiZBRp/6XVKn7RgyEBmXE6aZo3P0cxx2crN8B30KdNSvBqX43Xs4GOtVWNHr3Y1dIQ+6jsUsIM3Cd7Z0KGdDR0qyPRp0aQCzZ2QS4BwiF7ZVq8bHlijvc1dA85NLsrUTy85RsdOKnChMhxM25rHdOXnvqr/vFGt3sCB/zeaO3v1wMrdOm/BBE0qJFA4FD97ZsuAGWIXLa7Q+QuZGXYophRlyuc1jr+H2/e1a14ZS5gSGaEMAABAlD26vkre876hlLTBG15OL8nS8dOKVJyddsRfyxizf3bNMRPz1dUb0ObaNm2oahk0QJCkxo5ePb2hVq/taNCSyQU6qiyX/hpD6PYH9NOnNuvX/92qwbKuDy6p0DfPn6dsmvnGtGnF2bp48UT9a+1exyyPHn9Q/1xTqdNnj2OHrIN4dVu9fv7MZsfY5KJMfeu981yqKP6keD2aXJilLf22w95WRyiT6PjXAQAAIEo6evz6zr/f0p9X7JYZJJCZVJipE6YXqTQ3fZC7R0e6z6sF5XlaUJ6n5s5evbm3Wesrm9XVO3CHp9Yuv57dWKcVOxq1ZEqB5k3IVYqXcKbP2t1N+uJf12pTTduAc3kZPt38gQU6d8EEFyrD4SjJSdOlS0PBTG1r9/7xoJWe3lCrps5enTS9iKV9g6ht6dJ197+u/puMpXiMbrv0WALJEZpW4gxldjV0qDcQlI+fvQmL/0MAAACioLKpUx++8zVtrh34C3xOeoreOXd81JdI5GX4dOL0Yi2dUqiN1a16fXfToA1/27r9em5jnVbuaNTiyQWaX5bc4UxXb2h2zG+Xb9UgO13rhGlF+vElCzUhLyP6xeGIZKWl6KLFFXrizRrHL8aStGpno5o7evWueeP5Bbmf3kBQn7lvter6BVmSdMO7ZumYifnuFBXHphRnyRjtn3nnD1rtbujQtJJsdwvDmOGnCQAAwBjbXNOqC3/50qCBTM/WV3X5skmu9qzweT2aX56nK46bpPcdU6ayvMFn6rR1+/XfTXW6+6Uden1Xo/yBgbNrEt2KHQ0672fP69f/HRjI+LxGX333HP3pY8cRyMQxn9ejcxeUasnkgT2AttS16W+r96i9m52Z+tz86Aat2NHoGDt1Vok+cep0lyqKbxk+r8oifn5srWt3qRpEAzNlAAAAxtCqnY265u4Vau7sdYzb3i7VP/5LpdS8qbSPX+FSdU7GGE0uytKkwkztaezUq9sbVNnUOeC69p6Alm/ep5U7G7VkcoEWlOcl/MyZfW3duvnRDfrb6j2Dnp9XlqsfXrRQR5XlRrkyjAVjjE6aUay8TJ+e3VDrCOBqWrr1wMrdeu/CslHp+xTP/rmmUne+uN0xVp6fodsuOYYm4UdgWkmW42fv9n3tClorD0vnElJM/etpjHmfMebHxpj3uF0LAADAkXp2Q60uv+OVAYHMzHHZCjzyPbW/+YxLlQ3PGKOJhZm6aHGFLlxUroqCwWd9dITDmbsSeOZMIGh178s7dMatzw0ayPi8Rl941yz94zMnEcgkoPllebrgmHKlpjh/bWrt8usvK/doZ33yzmDYWN2qr/xtvWMsNcWjX1+xWAVsI35EpkcsVersDah6iMbsiH9RC2WMMXcaY359kMvaJF0v6StjXxEAAMDY+ffavfrYPSsHNNBdNClff/nkCVJbnUuVjUxFQaYuXFShixZVaOJBwpm7X9qh1bsa1eNPjHDmxS37dMEvXtA3/vmmWroGLlc5uiJPD//PKbrujJn0GElgkwozdcmSicpNdy4y6AkE9c+1e7VuT5M7hbmosb1Hn7h3pTp7A47x710wXwsq2CnoSOVl+FQUEWxt25e8AWCii+bypQ9L6pL0yWGueTl8nD/m1QAAAIyR/26q0+cfWKNARNOR02eX6JeXL1ZGqtelyg5feUGGPlBQob1NoWVNuxo6BlzT3hPQ85v3acX2Bi2cmK9jJuYr3Rd/3+sblc265T8b9PzmfYOez05L0Q1nzdJVJ0xO+GVbCCnMStUlSyfq4XVVquo3Y8Fa6dmNdWrq7NXJM4qTY3mJ16eP3bNSO+qdPwMuWzZRH1w60aWiEs+0kizV92u8vq2uTSfPKHaxIoyVmOopY61tN8YEJA3cIxIAACAOrNndpE/9cZX8EYHMB44t1y0XHR33MyrK8jP0/mPLhw1nuvxBvbq9Qat3NWpeWZ4WVuQpPzP2lzNsqW3Vz57eon+t3TvkNRccU6b/PXeuxo3htuWITZmpKfrAseV68q0abYpo2v36rtDOZe+eV6q0OAwiD5nxyHPSNVq109nY9+iKPN10/jyXikpM04qzHQ2UGzt61djew9KwBBRToYwxZoIkr6Rat2sBAAAYqa11bfrIXa+po8c5pf+qEybrW+fPkyeBGl8eSjjTG7Bas7tJa3Y3aUpRphZOzNfkwkyZGJpNYK3Vq9sb9Lvl2/T0hqFfgs4Yl63vXDBPJ07nnepkluL16Jz5pcrbVj9gx6Gd9R3684rdOn9hmQoT9BfngjM+Ks+kRY6xCXnp+s2Vi+NyVlwsG5+bpqxUr9r7/XuybV+7Fifo361kNiahjDFmkqQpg5zyGGNOkTTYv8R5kj4vyUqKza53AAAAQ6hu7tJVv39NjR3Opr7vXViWcIFMf33hTFVzp1bsaNT2Ifoe7Kjv0I76DuVn+DS3LFdzxucoN8MX5WoP6OwJ6PE3q3XXi9u1dk/zkNeNy0nT9e+cpQ8uqWCpEiSFGmGfOL1Y+RmpenpDjWNnpqbOXj2wYrfOnj9e04qzh36SOJQ27yxlLLnAMZaTlqK7PrKULeDHgDFGU0uy9EZly/6xrXVtWjzIVu2Ib2M1U+Yjkr45yLhP0nPD3GckNUu6aQxqAgAAGBMtXb26+s7XBmwffcrMYt168cKEDWT6m5CXofcuzNC+tm6t3NmoTdWtsoNc19TZq5e31uvlrfWakJeuOaU5mjEuW5mpYz+BOxC0enlrvR56fY8ef6Pa8Q50pJz0FH3qHdP1kROnxmUPIIy9o8pylZfp0yPrqhwNb3sCQf17bZWOn1qoZVMLY2pm2OHaUNWi9OM+6BjzeY1+c+VizSll17GxMr042xHKVDV3qaPHH5Wfl4iesfqv2SRpV8TY5PAxcrxPp6RVkr5nrd08RnUBAACMKmutvvDgWm2saXWML6zI06+vWDxgK91EV5ydpnPmleqEaUVau6dJb+1tUfcQuzFVNXepqrlLz26sU0lOmiYVZmpSYabK8tJHbVZKVXOnXtpSr5e31Wv5pjrVtnYPe31ehk9XHj9ZHztlalz0wYG7yvMzdNmyUAPgyL9br2xv0N7mLp09b3xc/xL95t5mPfV2rYxx/j/5w4sW6kQaz46pisIM+bxGvYEDEff2fe2aV8YOV4lkTH46WGtvk3Rb/zFjTFBSt7V26lh8TQAAADf86r9b9cRbNY6xacVZuvPDS5WVFr+/iB2pvAyfTp1ZohOmFWlDdavW7m5y7CQSqa61W3Wt3Vq1s1Fej1FhVqqKs1JVlJ2mlPJ5Sgn6pLQs+QPBAYFNIGjV3uPX7oYObd/Xrm117dq+r11rdjcNuZwq0sTCDH3s5Gm6eElFXP8CjejLSffposUVeurtGm2qcTYA3tXQoftf261z5peqPD/+lvis29OkZzfWDRj/0jmz9b5jy12oKLmkeDyaXJilLXUH/l7tqO8glEkw/IsDAABwmF7YvE+3Pr7RMVaQ6dMfrlmmouw0l6qKLT6vRwvK8zS/LFd7m7u0obpFW2ra1DXE7BkpFLL0hTRSq7LPuUF93Tlm/O9jyklLUUaqV129AXX5g+oZ5rmG4zHSSTOKdenSSTpnfqm8SbDMDGPD5/XonHmlKslp1Etb6h1L99q6/frb6j06cXqRFk8qiJvlTKt3NQ66LXzwrSf1qZvPdaGi5DSlONMRyuxu6FAwaJNiWWyyIJQBAAA4DJVNnfrsn193NPk0RvrZZcdqYmGme4XFKGOMyvMzVJ6foXfMGqed9e3aUN2qHfXtjqn5h6K126/Wbv9h1zK/PFfvO6Zc711YxtbWGDXGGC2ZXKjS3HT9J6JnkbXSi1vqtWNfh846arzyXGxyfTDWWq3Y0aiXt9UPONf80gPK2vaMjPlp9AtLUpMLsxyPu/1BVbd0qSwOZ15hcNEMZe6RNPScVQAAgDjR7Q/o039cpYaI5ThfeNdsnTKzxKWq4ofXYzStJFvTSrIVCFpVNXdqV0OHdjV0qKZl+J4vhyMvw6fjpxXqhGlFOmVWiaaXJNauOIgtFQWZumzZJD3+ZrV2Nzqbf1c2depPr+7UyTOKtaA8L+ZmzfT4g3rq7Rptrm0bcK5z1d/V9Py9yipn2VI0ZaenqCg7VfVtB/692dnQQSiTQKIWylhrPxytrwUAADCWvvvwWwO2UX7n3PH61GnTXaoofnk9RhUFmaooyNSJ06Wu3oDqWru1r61b9e092tfWrep9TTKpB/8FxBipLC9D00qyNLU4S9OKs7RkSqHmTshlaRKiKistRe87tlyvbW/Qq9sbHOd6A1bPbqzT1rp2nTlnnKtbw/fX2NGjh9dVDQibJemkGUV65PcPu1AVJGlyYaYzlKlv1wnTilysCKOJ5UsAAAAj8PTbNfrjK87NJKcUZerHlyTH1tdjLd3n1cTCTMcSsG9/6Ao1N9arfMpMPfPiq2rq6FVnb0AZPm/oI9WjtBSvSnLSlO5j+2rEBo8xOn5akSoKMvTkWzVq6XIuudvV0KF7XtmpxZMKtGRKgXyjtOPY4dha16Yn3qxRT2Bgf6bTZpXomIn5esSFuhAyuShLq3c17X9c09K9/2cg4l9UQxljzHxJX5O0QFLWMJdaay1vNQEAgJhS39atL/9tvWMsw+fVr69crNz02Hi3O2EF/FJXi2aNz3G7EmBEKgoydflxk/X85jq9sbfFcS4QtHptR4PeqmrRyTOKNWt8dlSXNHX0+PXilnq9VdUy4FxaikdnzyvV1OLhfm1DNJTlpyvFY+Tv18RsV32HZpfy8zARRC2UMcacIemx8Nc82E+akXV7AwAAGGPWWn3t7+u1r83Z8+Sb5x+lOaW5LlUFIB6kpnh05tzxmj4uW0+9XaP27oDjfFu3X/95s1prdqdryZQCTSvOGtNwJhi0WlfZrJe31Q+6e1lxdqrOWzBB+ZmpY1YDDl2Kx6OKggztqO/YP7azoZ1QJkFEc6bMtyX5JDVL+qGkVZK6ovj1AQAADtvfVlfq8TdrHGNnzhmnS5dOdKkiAPFmSlGWrjhusl7ZVq91lc2yEW9FV7d06eF1VcrP9GnRpALNLc1Ryiguawpaqx317Xppa72jR0l/s8fn6My541xdToWBJhdlOUKZXfUdstbGXLNojFw0Q5ljFZoBc7m19tEofl0AAIAjsqexQ9/615uOscKsVN184QJeEAMYkXSfV++YPU7zy/O0fFPdgB2aJKmpo1fPbKjVy1vrNXNctqaPy1Z5fsZhN6xu7erVm3tb9ObeFrUNsZ28z2t00oxiHR2Du0JBmlyU6Xjc3hPQvrYeleSkuVQRRks0Qxm/QqHM41H8mgAAAEckGLT6wl/WDvhF5v+9f4HG5aS7VBWAeFecnab3H1uubfva9fzmfWru7B1wTWdvQOsqm7WusllpKR5NK85SeUGGCjJTVZiVOmRj6/Zuv2paulTT0q2qlk7taegctj/EnNIcnTyjWFlp7AMTq/IzfMpNT3E0jN7Z0E4okwCi+X/d25KWSaqQtDOKXxcAAOCw3fPyDr2yzbml7YWLKnTO/FKXKgKQKIwxml6SrSlFWdpc26pVOxu1b4hlRd3+oN6ubtXb1a37xzJ8XmWnpShobfhD6vEH1dkbGPQ5IhVnp+ods8apvODgW87DXcYYTS7K0vrK5v1jO+s7tGRyoYtVYTREM5T5maQ/SfqupKui+HUBAAAOS1Vzp374+EbHWHl+hm5671EuVQQgEXk9RnNKczV7fI52NXRo1a5G7W4YuKwpUmdv4JADmP7G5aTp6Io8zS3Nlecwl0Qh+iYXZTpCmb1NnerxB5WaQv+feBa1UMZae78x5jhJnzXGtEn6vLW2+2D3AQAAuOVb/3pT7T3OX3h+ePHRbH8NYEz0zYaYXJSlxvYebalr05baNtW2HvmvTT6v0ezSHC0oy9O4XJZexqOKggx5jNS3M3bQSnuaOjStONvdwnBEorkl9mclbZO0VtInJL3PGPMPSVslDVhAaa39WbRqAwAAiPTEm9UDdlv64JIKnTi92KWKACSTgqxULc0q1NIphWrt6tXWunbtaexQY0evmjp69v9iPhSf12hcTrrG56ZpfG66phRlMaMizqWleDUhL0OVTQdmUe2sJ5SJd9FcvvRTaX9/KSOpVKFwZiiEMgAAwBVt3X7dFLHbUlFWqr527lyXKgKQzHLSfTpmYr6OmZgvKdSAvLmrV43tPeryB+UxktcYeTxGHmOUl+FTQaaPXZQS0KSizAGhDOJbNEOZ5dKwTb8BAABiwq2Pb1RVc5dj7Ovvmav8zFSXKgKAAzweo4LMVBXwMynpTCnM1Mtb6/c/bu7sVXNnr/IyWFYbr6LZU+Yd0fpaAAAAh2vt7ib94eUdjrFTZhbrfceUu1MQAABhJTlpSvd51NUb3D+2p7FDeRl5LlaFI8GiQgAAgLBA0Oprf18v229ub1qKR99733yWAQAAXGeMUUVBpmNsd+PBd+pC7CKUAQAACPvzil16c2+LY+yzZ87U5KIslyoCAMBpYkGG4/Gehg5ZS6eQeBXN3ZcCB79qP2utjWa/GwAAkOSaO3p16+MbHWOzxmfr46dOc6kiAAAGmljonCnT3hNQY0evCrPoMRSPojlTxozggxk8AAAgqn7y1CY1dvQ6xr51/jz5vLwsAQDEjvwMn7LTnHMYdjeyC1O8iuarjKmDfMyUdKZC2193SaqUdGr4HAAAQFRsqmnVva/sdIydM69UJ84odqkiAAAGF+orE7mEib4y8Sqauy/tHOLUVknPGmN+L+lxSXdKWhqtugAAQHKz1uo7/35LgeCB9fipKR7973lzXawKAIChTSzI1Ibq1v2P9zSG+srQlD7+xMx8XGvtekmXSpoh6WsulwMAAJLEE2/V6IUt+xxjnzh12oA1+wAAxIrImTJd/qDq2rpdqgZHImZCGUmy1i6X9LakD7hdCwAASHxdvQF975G3HGMT8tL1qXdMd6kiAAAOLjfDp7wMn2OMJUzxKaZCmbCApHK3iwAAAInvzhe3a3fEi9ivvHuOMlPZBBIAENsit8am2W98iqlQxhhzuqSjJPG3CQAAjKn6tm796tmtjrGlUwr03oVlLlUEAMChi1xmW9nU6eiPhvgQtbeBjDHfHOa0V9JsSe9XaEvsZ6NSFAAASFq3P7NFrd1+x9g33zOPJokAgLhQnu+cKdMbsKpt7dKEvIwh7kAsiubc3G9JGi6263sFtFPSl8e8GgAAkLR27GvXHyO2wH7fMWVaUJHnUkUAAIxMVlqKirJSVd/es39sd0MnoUyciWYoc4+GD2U6JK2XdI+1luVLAABgzPzw8Y3y998C2+vRF86e7WJFAACM3MSCTGco09ihZVMLXawIIxW1UMZa++FofS0AAIChrN7VqEfWVznGPnzSFFUUsAU2ACC+VBRmaM2epv2Pq5q75A8EleKNqfaxGAb/pQAAQNKw1urmR992jOVl+PSZd8xwqSIAAA5fRX6G+ndCCwStqpq7XKsHI+fKfo/GmKmS3iup7xXQFkn/stZud6MeAACQHJ54q0YrdjQ6xv7njBnKy/S5VBEAAIcvzedVSU6aalu794/tbuwYsDMTYldUQxljjEfSrZKuU2jHpf5+aIy5XdIXrbXBaNYFAAASnz8Q1C3/2eAYm1iYoStPmOxSRQAAHLmJhZmOUKayqdPFajBS0V6+dLekzykUBnVIej380REeu17SnVGuCQAAJIG/rtqjbXXtjrEvnj1HaSmR7xMBABA/IrfGrmnulj/APId4EbVQxhjzXklXSApK+pKkQmvtEmvtEkmFkr6o0O5MVxpjzo9WXQAAIPF19QZ029ObHWMLyvP0ngUTXKoIAIDRUZaf7uwrY62qW+grEy+iOVPmUwqFLjdba2+11vb2nbDW9lprfyTpJ5JM+FoAAIBR8cdXdg5ofPilc2bL4zFD3AEAQHxISwn1lemPJUzxI5qhzLLw8bZhruk7d9wY1wIAAJJEa1evfvHsFsfYCdOKdPKMYpcqAgBgdJVFLGEilIkf0QxlMiUFrbX1w1yzT6HZNLSKBgAAo+KO57ersaPXMfbFc2bLGGbJAAASQ2RfmaqmLgWC1qVqMBLRDGV2SvIYY04Z5pqTFVq+tDM6JQEAgERW39atO57f5hg766jxWjSpwKWKAAAYfZGhjD9oVdtKX5l4EM1Q5mGFApefGWNyIk+Gx25TaKbMv6JYFwAASFC/fG6r2nsC+x8bI33hXbNdrAgAgNGXkepVUVaqY4wlTPEhmqHMDyTVSzpa0jpjzJeNMeeFP74saa2kueFrbo1iXQAAIAHtberUva84J9++75hyzS4d8N4QAABxb0BfmUZCmXiQEq0vZK2tNcZcKOmfkiZL+n8RlxhJLZIutNbWRqsuAACQmG5/ZrN6/MH9j1M8Rp9/5ywXKwIAYOyU52dofWXz/sd7m7oUtFYeeqjFtGjOlJG1drmkRZLuVSiAMToQxtwraZG19vlo1gQAABLPrvoO/WXlHsfYZcsmaVIRewkAABJTeYFzpkxPIKh9bd0uVYNDFbWZMn2stdslXW1CWx4UhYfrrbW0hgYAAKPi9mc2y99v14m0FI+uO2OGixUBADC2stNSlJfhU3PngR0HKxs7NS4n3cWqcDBRnSnTnw3ZF/4gkAEAAKNi+752PfR6pWPsiuMna3wuL0oBAIktchcmmv3GvjENZYwxpxljbjHG/MMYc2XEuenGmMeMMfcZY75mjFkylrUAAIDkcPvTmxXoN0smw+fVJ0+b7mJFAABER0XBwFCGORCxbUxCGWNMmTHmOUnPSPqCpPMlTY24LFvS2ZIukfRdSa8aY54yxkwYi5oOhzHmTGPMo8aYOmNMjzFmjzHmOWPMNcYY7xD35Bhjvm+M2WqM6TbG7DDGfNsYkxbt+gEASDZbatv0jzXOWTJXnThZJTn8MwwASHyRM2W6eoNqaO9xqRocilHvKWOMyVEojOnb3uBtSRsU2vK6vwZJDyjUV+ZoSeMknS7pCWPMcdbajtGubSSMMZ+S9AuFGhFLUlBSefjjNEmXGmMusNZ29runQNJ/JS0ID1mFdpr6pqSTjTFnW2v9UfoWAABIOrc9vVn9JskoK9WrT5zKLBkAQHLISU9RdlqK2roP/NpZ2dSpomzenIhVYzFT5ssKBTJvSzrGWjvPWnuhtfaf/S+y1u621l5mrX2XpAmSLlRoF6ajJN04BnUdMmPMbEk/k9Qr6VpJhZJ8kiZJ+ookv6SzNLDOBxQKZDolXSMpXdJESU9KOmOQ6wEAwCjZWN2qh9ftdYx9+KQpKsxKdakiAACiyxgz6BImxK6xCGUuUmiGyFXW2nWHckO46e/fFQpAjKRLx6CukbhUoVlEv7HW3mGtbbTWBsNB0i2Sbg9fd37fDcaY8xQKaiTpWmvtXdbaHmvtHkkXS2qU9C1jTJEAAMCou+3pTeq/bD4nLUXXnjLNvYIAAHDBYM1+6SsTu8YilJksKWitXXUY9z6k0DKhyP4z0VYePm4e4vyu8LH/3/ZPho/rrbV/6n+xtbZZ0h0KzZw5d7SKBAAAIW/tbdGj66sdY9ecPFX5mcySAQAkl8hQpr074NgmG7FlLEKZJkkeY8zkw7i3WKGa2ka1opHr6xA4VIDynvCxf/B0Svj44BD3PBo+vvsI6gIAAIP46VObHI9z01N0zcluv8cDAED05Wf6lOFz7kuzt7nLpWpwMGMRyrwQPv7AGGOGvXKgb4SPr45iPYfjT5LaJZ1jjPmVMWamMSbDGDPHGHOXpDMV6hvzY0kyxpRIygvf+/IQz9m3lGvOSAoxxlQM9yGpdKTfHAAAiWT9nmY98VaNY+zaU6YpL8PnUkUAALjHGKOy/HTHWBV9ZWLWWIQyP1RoCdJFkv5rjHmnMWbYXZ6MMbONMX+U9BmF+tH8fAzqOmTW2i0KzWjZrtCypE2SOhRqXvzh8PFMa+368C39+8Q436o78JwNCjUInjLCcnYf5GPFCJ8PAICE8pOIWTL5mT59+KQp7hQDAEAMKItYwrS3iZkysWrUt8S21r5mjLleod2LTpL0uKROY8xGhUKEdoV2NUpXaLnSLB3o4SJJv7XWPj7adR2GXIVqHEyHQjtG9em/v1jjMM/ZJKnYGJNmre0+svIAAMDruxr1zIZax9gnTp2unHRmyQAAkldZnjOUaejoUWdvYMCyJrhv1EMZSbLW/sIYs13SbZKmS8qUdKykYwa5vG+JU0DSLZK+ORY1jYQx5h2S/imp72/sdoUCpWmSKiQtlvQ3Y8yXrbU/UKj2Ph3DPHVP+Jgh6VBDmYkHOV8qZssAAJLUT55y9uQvykrVVSccTls7AAASR0lOmlI8Rv7ggV2Xqpo6Na0k28WqMJgxCWUkyVr7qDHmcUlnKNQYd6GkGQrNQMlQqCdLo6S3FOpDc4+1dvdY1TNCP1EokNkr6UPW2v9KkjHGI+kSSb+UlC/pm8aYOxT6XiTJb60NDvO8fecyFZo1c1DhLbWHNPK2PQAAJIaVOxq0fFOdY+yTp01XVtqYvbwBACAueD1Gpbnp2tOvl8ze5i5CmRg0pq9arLUBSU+GP+JCeNeoY8IPr+wLZCQpHLjcb4xplPSYpCxJp0paHr4kxRgz3lrr7DZ4QEHflxn1wgEASDKRvWSKs9N0xfHMkgEAQJIm5EeEMjT7jUlj0eg33vX1t2mV9OxgF1hr/xM+L0kTwk1828OPB91/0xiTpVCII7m/5TcAAHHtlW31enFLvWPs0++YroxU1soDACANbPZb29Itf2C4hR1wA6HMQH3hSq+11g57ZUhfY99V4eOSIa47PnxssdY2H25xAAAkO2utfvykc5bM+Nw0fei4SS5VBABA7JmQ59y3JmCtalvZbybWEMoMtFmhrasLjTHlg11gjFkkKSf88KXwsW+J1qVDPO9Z4ePq0SgSAIBk9dLWer22vcExdt3pM5TOjhIAAOyXluJVcXaqY4wlTLGHUCaCtbZD0oPhh1+LPB9ehvSL8MO/Wmt3hT+/V6FGvicZY86PuKdE0rXhhw+PetEAACSJwWbJlOWl64NLD7ZZIQAAySdya+y9zV0uVYKhEMoM7rOS1kr6tDHmNWPM/xljvmqM+Y2kXQotRdoo6VN9N1hrd0r6c/jhfcaYy40xGcaYxZIel1So0I5Ld0Xx+wAAIKEs37xPq3Y2OsauO2Om0lKYJQMAQKQJ+c4lTFVNnTq0Lh2IFvaMHIS1tt4Yc7xCs1sukvQJSXkKbX29RdLPJP10kN4w10k6SqHdm/4YcS4o6ePhpsAAAGCEBpslU1GQoYsWV7hUEQAAsS2y2W+XP6iG9h4VZae5VBEiEcoMwVrbJen28Meh3tNojDlZ0ncUCnL6dlvaIOl6a+3jo14oAABJ4pkNtVq7u8kx9tkzZyo1hYm/AAAMJjfdp+y0FLV1+/ePVTV3EcrEEF7FjDJrbbu19kZJ4xXaiWmWtXYugQwAAIdvsFkyk4sy9YFjB+3JDwAAwsoiljDR7De2EMqMkXA4s8pau9ntWgAAiHdPvFWjN/e2OMY+d+ZMpXh5KQMAwHBo9hvbeCUDAABiWjBo9ZOIWTLTSrL03oVlLlUEAED8iOwr09zZq/Z+y5ngLkIZAAAQ0x59o0obqlsdY8ySAQDg0BRlpyo14t9MljDFDl7NAACAmOUPBAf0kpk5LlvvOZpZMgAAHAqPMZqQF9FXhiVMMYPdlwAAQMz6++uV2lbX7hi74axZ8nqMSxXBDW1N9ZKkqqoqVVSwBfpwqqqqJEnt7e0HuRJAMpmQn66dDR37H1c1M1MmVhDKAACAmNTjD+q2p5398ueX5+qc+aUuVQS3BIPB/cfKykqXq4kPNmjdLgFADJkQ0ey3rrVb/kCQpcAxgFAGAADEpAdW7NKeRuc7eTe+a7aMYZZM8jLKKx7ndhExrXlfjdslAIhBpbnpMpL64tqglWpau1Ue0QQY0UcoAwAAYk5Xb0C3P7PFMbZkcoHeMavEpYoQC7xpmbrpvuVulxHTbjxnrmx4ZhEA9ElN8agoO1X72nr2j1U1dxLKxADmKgEAgJhz78s7Vdva7Rj7wtnMkgEA4HBFLmGqptlvTCCUAQAAMaWt269f/XerY+yUmcU6flqRSxUBABD/BuzA1NQla+k/5TZCGQAAEFPufGG7Gtp7HGM3vmu2S9UAAJAYIkOZzt6AWrr8LlWDPoQyAAAgZjR19Oh3y7c5xt45d7yOmZjvTkEAACSIvAyfMnxex1hVE1tju41QBgAAxIzfLN+m1u4D79oZI934rlkuVgQAQGIwxgyYLVNFXxnXEcoAAICYUNfarbtf3OEYe8/RZZo7IdedggAASDClkaFMC6GM2whlAABATPjlc1vU2RvY/9jrMfr8O2e6WBEAAImlLGIHpn2t3erxB12qBhKhDAAAiAF7mzr1p1d2OcYuXFSuaSXZLlUEAEDiGZebJo858NhKqmG2jKsIZQAAgOtuf2azegIH3qnzeY0+eyazZAAAGE0+r0fF2WmOMfrKuItQBgAAuGrHvnY9uHKPY+yyZZNUUZDpUkUAACSugc1+2YHJTYQyAADAVT99apMCQbv/cbrPo+tOn+FiRQAAJK4JEX1lqpu7ZK0d4mqMNUIZAADgmo3Vrfrn2r2OsatPmKJxuelD3AEAAI5E5EyZLn9QTR29LlUDQhkAAOCa7z/2tvq/OZedlqJPnjbdvYIAAEhwOekpykrzOsboK+MeQhkAAOCKl7fW69mNdY6xj50yVQVZqS5VBABA4jPGaEKucwkTfWXcQygDAACizlqr7z/2tmOsODtN154yzaWKAABIHgOb/TJTxi2EMgAAIOoeWV+ltXuaHWPXv3OmstJSXKoIAIDkMSHfGcrUt/eo2x9wqZrkRigDAACiqscf1A8f3+gYm1acpUuWTnSpIgAAkktJTpq8xjjGqpkt4wpCGQAAEFX3vbpTO+s7HGNfOme2fF5elgAAEA0pHo/G5aY5xljC5A5e/QAAgKhp7erVz57Z4hhbNClfZ88rdakiAACSUyl9ZWICoQwAAIia3y7fpob2HsfYV8+dKxMxhRoAAIytyGa/1c1dsta6VE3yIpQBAABRUdnUqd8u3+YYO+uo8Vo6pdCligAASF4T8pzbYvcEgqqPeOMEY49QBgAARMUP/rNB3f7g/sdej9GXz5ntYkUAACSv7LQU5aQ7dz2k2W/0EcoAAIAxt2pno/65Zq9j7EPLJmnGuByXKgIAAJFLmPY2d7pUSfIilAEAAGMqGLT67sNvOcZy0lP0+bNmuVQRAACQBi5hYqZM9BHKAACAMfXvdXu1ZneTY+xzZ85UYVaqOwUBAABJA2fKNHb0qrM34FI1yYlQBgAAjJnOnoC+/9gGx9jU4ixddcIUdwoCAAD7FWenKcXj3AGR2TLRRSgDAADGzG+Xb1NVxIu7r507V6kpvAQBAMBtXo/R+FznbJkq+spEFa+IAADAmKhq7tSv/7vVMXbSjCK9c+44lyoCAACRSvMiQxlmykQToQwAABgT33vkbce6dI+Rvn7eUTLGDHMXAACIpsi+MjUtXQoGrUvVJB9CGQAAMOpe2LxPj6yrcoxdsnSS5k7IdakiAAAwmMhQpjdgta+926Vqkg+hDAAAGFU9/qC++a83HGN5GT598ezZLlUEAACGkpmaorwMn2OMJUzRQygDAABG1e9f2K5tde2OsS+dM5stsAEAiFGRs2UIZaKHUAYAAIyavU2d+tnTmx1jCyvydOnSSS5VBAAADiYylGFb7OghlAEAAKPme4+85Wjua4z0nQvmy+uhuS8AALFqQl6G43FzZ6/au/0uVZNcCGUAAMCoWL6pTo+ur3aMXbZskhZOzHenIAAAcEiKslPl8zrfQKluYbZMNBDKAACAI9bVG9A3/+ls7luQ6dMX30VzXwAAYp3HGI3Ppa+MGwhlAADAEfvpU5u1o77DMfblc+aogOa+AADEhbKIJUxVTZ0uVZJcCGUAAMAReaOyWb97fptj7NhJ+frgkokuVQQAAEaqNKLZb01rtwJB61I1yYNQBgAAHDZ/IKivPLTO8aLN5zW65cKj5aG5LwAAcSNyB6ZA0KqurdulapIHoQwAADhsv39hu96obHGMfeb0GZo1PseligAAwOFI93lVkOlzjLE19tgjlAEAAIdlx752/fjJTY6xmeOy9al3THepIgAAcCQilzBVNdNXZqyluF0AAACIP9Zafe3v69XtD+4fM0b6/oVHKy3F62JlAICx0NZUL0mqqqpSRUWFy9XEtqqqKklSe3u7y5WM3IS8DL1d1br/MTswjT1CGQAAMGJ/enWXXtpa7xi7+oQpWjy5wKWKAABjKRgM7j9WVla6XE18sHHYJDeyr0xrl1/t3X5lpREdjBX+ZAEAwIhs39eu/3vkbcdYWV66vnD2bJcqAgBEj1Fe8Ti3i4hpzftq3C7hsBVmpSrV61FP4MBM2KrmLs0Yl+1iVYmNUAYAABwyfyCoGx5co87egGP8/31ggbJ5Fw0AEp43LVM33bfc7TJi2o3nzJUNBg9+YQzyGKPxeWna3XCgl0xVcyehzBii0S8AADhkv/7vVr2+q8kxdvlxk/SO2bxrCgBAIpiQm+F4TF+ZsUUoAwAADskblc366VObHWNTijL1v+fNdakiAAAw2iL7ytS2disQh/1x4gWhDAAAOKiu3oA+/8Aa+fu9KPMY6ceXHKPMVJYtAQCQKCK3xQ4Erepau12qJvERygAAgIP6wX82anNtm2Ps0++YoUWT2G0JAIBEku7zqiDT5xirau4c4mocKUIZAAAwrCffqtGdL253jM0ry9Vnz5zpUkUAAGAsTchz9pWppq/MmCGUAQAAQ9rT2KEv/GWtYyw1xaOfXHKMUlN4GQEAQCKK7CtT1UIoM1Z4NQUAAAbV4w/quvteV3Nnr2P86+fN1azxOS5VBQAAxlpkX5nWLr/auv0uVZPYCGUAAMCgfvj4Bq3Z3eQYO2/BBF15/GR3CgIAAFFRlJWqVK8zLqCvzNgglAEAAAM8+VaNfve8s4/M5KJM3XzhAhljXKoKAABEgzFmwGwZ+sqMDUKZQ2BCnjDGWGPMcmPMoH9uxpgcY8z3jTFbjTHdxpgdxphvG2PSol0zAACHa3fDIH1kvB794kOLlJvuG+IuAACQSCJDmSpCmTGR4nYBceIzks6S1C7pI9baYOQFxpgCSf+VtCA8ZCVNlvRNSScbY8621rIIDwAQ09q7/br2npUD+sh84/yjNL88z6WqAABAtEU2+61t7VYgaOX1MGN2NDFT5iCMMbMk3RJ++CVr7dYhLn1AoUCmU9I1ktIlTZT0pKQzJN04xqUCAHBEgkGrGx5cow3VrY7x846eoCuOm+RSVQAAwA2luc5QJhC0qmvtdqmaxEUoMwxjjFfSPZIyJT0l6VdDXHeeQjNpJOlaa+1d1toea+0eSRdLapT0LWNMURTKBgDgsNz29GY9/maNY2zmuGx9/wP0kQEAINmk+7wqyHQuW6bZ7+gjlBneVyUdJ6lF0kettXaI6z4ZPq631v6p/wlrbbOkOxSaOXPuWBUKAMCReHR9lW57erNjLC/DpzuuXqIc+sgAAJCUJuRlOB7T7Hf0EcoMwRhzrEL9YCTpemvtrmEuPyV8fHCI84+Gj+8ejdoAABhNb+5t1o0POhv7ej1Gv7x8kSYXZblUFQAAcFtkX5mqFkKZ0Uaj30EYY1IVWrbkk7RdUqEx5qeSeiW9Jumf1tqe8LUlkvo6H748xFOuCx/njFXNAAAcjr1Nnfro3SvV2RtwjH/jvLk6aUaxS1UBAIBYELkDU2uXX23dfmWnESWMFv4kB/cFSfPDn0+Q9D5JHZKODp/bZIz5kLV2laT+fWI2DfZk1toGY4xf0pSRFmKMqTjIJaUjfU4AACSpqaNHV935mqoj3vW6dOlEXX3iFHeKAgAAMaMoK1WpXo96Agc2IK5q7tTMcTkuVpVYCGUiGGNKJX0t/PBlSR+w1laHz3kU6h9zm6RnjTEnyPln2DjMUzdJKjbGpFlrR9KyevcIrgUA4JB09gT00T+s1JbaNsf40ikF+s4F82nsCwAAZIxRaV66djV07B+rbu4ilBlF9JQZ6NOSsiT5JV3SF8hIkrU2aK39paTbJeVI+rak/vO9OzS0nvAxY5hrAAAYc/5AUP9z/2qt2ul8L2HGuGz97qolSk3h5QEAAAiJXMJURbPfUcWrroHOCB8ftdYONUvlofDxPZL69gTzW2uDQ1wvSX3nMkdYz8SDfCwd4fMBAJKYtVZf/8cbeurtWsd4aW66/nDNMuVnprpUGQAAiEWRzX5rW7sVCA61MTFGiuVLA5WEj68Oc01N+JimA6FMijFmvLW2Zoh7CsLHEc0Ht9buGe4808sBAIfKWqvvPvy2/rzC+Z5DbnqK/nDNMpXnM5kTAAA4leY6Q5lA0KqutXvADBocHmbKDNQSPjYNc03/BXStktrDn08d7GJjTJZCS6IkqW2wawAAGEvWWn3vkbd154vbHeOpKR7dcfVSzS5lbTgAABgo3edVYcRM2qrmziGuxkgRygz0Zvg43PbVx4ePldbaVkmrwo+XHOT6Fmtt8xHWBwDAiFhr9f8efVu/f8EZyHiM9LNLj9WyqYUuVQYAAOIBfWXGDqHMQI+Ej1cbY8oiTxpjciR9Pvzw3+Hjk+HjpUM851nh4+pRqRAAgENkrdX3H9ug3z0/MJD5ySXH6Jz5pS5VBgAA4kVkXxlCmdFDKDPQ3yS9LilX0hPGmDONMT5jTIox5gxJyyXNkNQl6Qfhe+5VqJHvScaY8/s/mTGmRNK14YcPR+MbAABACgUyNz+2Qb9Zvs0x7jHSjz94jC44ptylygAAQDyJDGXauv1q6/K7VE1iIZSJEN5B6SJJb0iaJ+kpSd0KbWn9tKRjFNoG+3Jr7fbwPTsl/Tn8FPcZYy43xmQYYxZLelxSoUI9au6K3ncCAEhm/kBQX/zrOv02IpAxRvrRBxfqfccSyAAAgENTmJWq1BRnfEBfmdFBKDMIa+02ScdJ+rikZyXtleSXVK/QbJfTrLUPRdx2naQ1krIl/VFSh6SVko5VaBbNx621DdGoHwCQ3Dp7AvrEvav011XODfyMkW69aKHef2yFS5UBAIB4ZIwZsAtTVQtLmEYDW2IPwVrbIel34Y9Dub7RGHOypO9I+oQO7La0QdL11trHx6RQAAD6aero0cf+sFIrdzY6xr0eox9ceLQuXEwgAwAARm5CXrp2NXTsf1xNX5lRQSgziqy17ZJuNMZ8U6Hdm1qstZtdLgsAkCT2NHbomrtXaFNNm2M83efRLz60SGfOHe9SZQAAIN5F9pWpbemWPxhUiocFOEeCUGYMhMOZVQe9EACAUbJiR4M+ee8q1bf3OMZz01N010eWavFktr0GAACHL3L5UsBa1bV2a0JehksVJQYiLQAA4twDK3bpQ797ZUAgU5qbrr9+6kQCGQAAcMTSfF4VZqU6xtga+8gxUwYAgDjlDwT1f4++rbte3DHg3Ixx2frDNctUns+7VwAAYHRMyEtXQ783gegrc+QIZQAAiEO1rV26/s9r9NLW+gHnTp9dotsuO1a56T4XKgMAAImqNC9db+5t2f+4qrlL1loZY1ysKr4RygAAEGde3LJPn/vzGu1r6x5w7hOnTtOXzpkjr4cXRwAAYHSVRfSPaev2q7XbzxtBR4BQBgCAOBEIWt329Gbd/sxmWes8l+r16OYPLGDLawAAMGYKMn1KT/Goyx/cP1bV1KXcUkKZw0UoAwBAHNjb1KkbHlyjV7Y1DDg3IS9dv7h8kRZNKnChMgAAkCyMMSrNS9eO+o79Y1XNnZpdmuNiVfGNUAYAgBhmrdVDqyv1rX+/qdYu/4DzZ8wZp1svXjhgNwQAAICxMCE/IyKUodnvkSCUAQDgIJYsWaLq6urof+G0bHmOu0KeSccOOGWDAQVf/7ue+ONTOvrrdpCb3VVaWqqVK1e6XQYAABhlZXnpjsd1bd3q8QeVmuJxqaL4RigDAMBBVFdXq7KyMqpfM2PWCSp612fkycofcM7fUqt9//qBuis3RLUmAACA8bnpMkb7+9tZK9W0dGliYaa7hcUpQhkAAA6R8XiUW1gytl8jM18ZJ1yu1CmLBj3fs/kldb5yn9J7OpVePH5MazkcLQ11ssHgwS8EAABxyef1qCQ7TbWtB3aBrGomlDlchDIAAByi3MIS3XTf8jF5bmut1lU266Ut9eoJDAw1MnxenTFnnGacebX0yavHpIbR8O0PnarmfTVulwEAAMZQWX6GI5TZ29zpYjXxjVAGAACX1bV269mNtUM2yptWnKUz5oxTVhr/bAMAAPdNyEvXmt0HHlc3d8laK2OMe0XFKV7dAQDgku7egF7eVq91e5o1WKveDJ9Xp84q1uzxObzIAQAAMWNCRLPfbn9QDe09KspOc6mi+EUoAwBAlFlr9VZVi17cUq/O3sCg1xw1IVcnzyxWhs8b5eoAAACGl5PuU3Zaitq6/fvHqpq7CGUOA6EMAABRtLepU8s316mmpXvQ83kZPp05ZxzN8gAAQEwry0/Xppq2/Y/3NndqfnmeixXFJ0IZAACioLmzVy9u2afNtW2Dnvd6jJZOKdDiSQVK8XqiXB0AAMDITMjLcIQyQ/XGw/AIZQAAGEPdvQGt2NGoNbubFLCDdY6Rppdk6dSZJcrN8EW5OgAAgMMT2VemqaNXHT1+ZaYSM4wEf1oAAIyBQNDqjcpmvbq9Yci+MfkZPp02q0RTirOiXB0AAMCRKclOU4rHyB888KZTdXOXppVku1hV/CGUAQBgFFlrtaO+Q89vrlNjR++g16SleHT8tCItKM+T18OuSgAAIP54PEaleena09i5f2wvocyIEcoAADBK6lq79fyWOu1u6Bz0vMdICyvytWxqodLZVQkAAMS5CRGhTFXz4K+BMDRCGQAAjlB7t18vb6vXm3tbhrxmekmWTppRrILM1ChWBgAAMHbK8jIkNe5/XNPSrUDQMhN4BAhlAAA4TP5AUKt3NWnlzgb1BgZv4jsuJ02nzixReUFGlKsDAAAYW6URzX4DQava1i5NyON1z6EilAEAYISstdpU06YXtuxTW7d/0Guy01J04vQizSnNkTG8WwQAABJPus+rwqxUNbT37B+raiKUGQlCGQAARqC6pUvLN9Wpqrlr0PM+r9HiyQVaNKlAPq8nytUBAABEV1leuiOU2dvcqUUqcLGi+EIoAwDAIfBmFynz1Gv0wIrdQ14zryxXJ0wrUlYa/7wCAIDkUJafoTf69dXb29Qlay0zhQ8RrxoBABhGV29AZsG5Kpt7tjyp6YNeU5GfoVNnlagkJy3K1QEAALirLN+5VKmzN6Cmjl4VZLG5waEglAEAYBDWWv17XZW+/+jb8i68YNBr8jJ8OmVmsaYVZ/FuEAAASEq56SnKSvOqvTuwf6yyuZNQ5hARygAAEGFLbZu+/o/1emVbw6DnU70eLZtaqIUT85TioW8MAABIXsYYleVlaHNt2/6xvU2dml+W52JV8YNQBgCAsK7egH7x7Bb9+r9bB93i2gaDWlCRrxOmFykzlX9CB9PWVC9JqqqqUkVFhcvVxLaqqipJUnt7u8uVAABwZMrzI0OZwTdEwEC8ogQAQNJzG2v1zX++qV0NHYOe79q5Vj2rH9KZP78vypXFl2AwuP9YWVnpcjXxwQYHBoAAAMSTyL4yzZ29au/2s/nBIeBPCACQ1GpauvSdf7+lR9ZXDXq+PD9Du/7xI9W8+qjyisdHubp4ZpRXPM7tImJa874at0sAAGBUFGWnKtXrUU8guH9sb3OnZo7LcbGq+EAoAwBISoGg1T0v79CPntiktm7/gPMpHqNrT52m/zljhmb9/CoXKoxv3rRM3XTfcrfLiGk3njNXNhg8+IUAAMQ4jzGakJeunf1mHO9t6iKUOQSEMgCApPNGZbO+8tA6vVHZMuj5pVMK9H/vX6BZ43khAQAAcCjK8jMiQplOF6uJH4QyAICk0eMP6ufPbNYvntuqwCB9PAoyffrquXN10aIKeTxscQ0AAHCoyvLTHY/r2rrV4w8qNYWdKodDKAMASArr9zTri39dqw3VrYOe/+CSCn3l3XNVmJUa5coAAADi3/jcdHmM1Pe+l7VSdUuXJhVmultYjCOUAQAktB5/ULc/s1m/HGJ2zMxx2fq/9y/QsqmFLlQHAACQGHxej8blpKu65cB22HubOgllDoJQBgCQsLbUtun6B14ftHdMisfoujNm6NPvmMG0WgAAgFFQlh8RyjTTV+ZgCGUAAAnHWqt7X9mp//fo2+rqHbi7zdwJubr14qM1ryzPheoAAAASU1l+hlbvatr/uLq5S8GgpVffMAhlAAAJpba1S1/8yzr9d1PdgHN9s2M+c/oM+bzMjgEAABhNE/KczX57A1Z1bd0an5s+xB0glAEAJIzlm+p0w4NrtK+tZ8C5GeOy9dNLjtH8cmbHAAAAjIXM1BQVZPrU2NG7f2xvUyehzDAIZQAAca83ENSPn9ykXz23ddDzHz5xir7y7jlK93mjXBkAAEByKcvPcIYyzV061sV6Yh2hDAAgrlU2deqz97+uVTsbB5wbl5OmH168UKfNKnGhMgAAgORTlpehN/ce2GShsrFT1loZQ1+ZwRDKAADi1rMbanX9A2vU3Nk74NyZc8bphxcvVGFWqguVAQAAJKeyfOdSpc7egJo6elXAa7JBEcoAAOJOIGh129Obdfszm2Wt85zPa/Tlc+booydP5R0ZAACAKMvL8Ck7LUVt3f79Y3uaOgllhkAoAwCIK43tPbr+gTWD7q40sTBDP79skRZOzI9+YQAAAJAxRmX56dpU07Z/rLKpUwvYbGFQhDIAgLjxRmWzPnHvKlU2dQ449+75pbrloqOVm+5zoTIAAAD0qcjPdIYy9JUZEqEMACAu/GvtXn3xL2vV7Q86xr0eo6++m+VKAAAAsaK8IMPxuK3br5Yuv/IyePMsEqEMACCmBYNWtz6xUb8cZLvr4uw0/eJDx+q4aUUuVAYAAIDBFGT6lOHzqrM3sH+ssrGTUGYQHrcLAABgKK1dvbr2npWDBjJLJhfokc+eTCADAAAQY4wxKs93zpYZbPk5mCkDAIhRu+o79NE/rNDm2rYB5644fpJuOn+efF7eWwAAAIhF5QUZ2lLnbPaLgQhlAAAxZ9XOBl17zyo1tPc4xlM8Rt967zxdcfxklyoDAADAoYicKdPc2avWrl7lsCmDA6EMACCm/GvtXn3hL2vVE9HQtzArVb+8fJGOZ7kSAABAzCvKTlVaisexSUNlU6fmlBLK9Me8bwBATLDW6ufPbNZn7399QCAzpzRH//zMSQQyAAAAccJjjMroK3NQzJQBALiuNxDU1x5ar7+s2jPg3Dtml+jnH1qk7DT+yQIAAIgn5fkZ2r6vff/jykZCmUi8wgUAuKq9269P/2m1/rupbsC5K4+frJvOP0opNPQFAACIO+UFzpkyjR296ujxKzOVKKIPfxIAANfsa+vWNXev0Lo9zY5xY6Svn3eUrjlpiowxLlUHAACAIzEuO00+r1FvwO4fq2zs1MzxOS5WFVt46xEA4Iod+9p14a9eGhDIpPs8+s0Vi/XRk6cSyAAAAMQxj8eoLI++MsNhpgwAIOrW72nWh+96TfURW14XZPr0+w8v1aJJBS5VBgAAgNFUVpChnQ0d+x8TyjgRygAAouq17Q265u4Vauv2O8YrCjL0h2uWaXpJtkuVAQAAYLRVROzAtK+tR129AaX7vC5VFFtYvgQAiJrnNtbqqjtfHRDIzCvL1UOfPpFABgAAIMGMy02T1+Nckr6X2TL7EcoAAKLi0fVVuvaelerqDTrGT5pRpAc+cYLG5aS7VBkAAADGSorHowl5ztd5u9kaez9CGQDAmPvLyt267r7Vjs77knTWUeP1+6uXKjuN1bQAAACJqiJia+w9jR1DXJl8CGVGwBhznDGmxxjz3BDnc4wx3zfGbDXGdBtjdhhjvm2MSYtyqQAQM+56cbu++Nd1CjrzGL3/2HL98vJFrCcGAABIcBUFmY7H+9p61NkTcKma2MJbk4fIGJMn6X5JviHOF0j6r6QF4SErabKkb0o62RhztrXWP9i9AJCIrLX6+TNb9KMnNw04d8Xxk/Sd986Xx8OW1wAAAImuNDddKR4jf7936ZgtE8JMmUP3W0lThzn/gEKBTKekaySlS5oo6UlJZ0i6cawLBIBYYa3VzY9tGDSQ+eRp0/XdCwhkAAAAkoXXY1SeH7mEib4yEqHMITHGXCvpgwrNfhns/HmSzgo/vNZae5e1tsdau0fSxZIaJX3LGFMUlYIBwEWBoNX//uMN/Xb5tgHnvnj2bH3l3XNkDIEMAABAMonsK7ObmTKSCGUOyhhzlKSfKhTI/GiIyz4ZPq631v6p/wlrbbOkOxSaOXPuGJUJADEhELT60l/X6b5Xdw04950L5ukzp89woSoAAAC4raLQ2VemsaNXJjPfnWJiCKHMMIwx6QotS8qUdKukR4a49JTw8cEhzj8aPr579KoDgNjSF8j8bfUex7jHSD+6eKGuOmGKO4UBAADAdeOy05Sa4owgUkpnu1RN7KDR7/B+Kmm+pJckfU3SyZEXGGNKJOWFH748xPOsCx/njLQAY0zFQS4pHelzAsBoGyqQSfV69LPLjtU58/lRBQAAkMw84b4y2/e17x9LKZsj6V/uFRUDCGWGYIy5SNInJNVLusRa6x+iB0L/PjEDO1pKstY2GGP8kqYcRim7D+MeAIia4QKZ31y5WKfPGedSZQAAAIglEwsiQpkJc12sJjawfGkQxpjJkn6nUB+Zq8INe4eS1u/zxmGua5JUYIxJG+YaAIgrBDIAAAA4VBUFzr4y3twSeXOT+/UiM2UiGGNSJN0vKV/SLdbaR4e/Q4F+nw/XPronfMyQ1D2CkiYe5HyppBUjeD4AGBUEMgAAABiJ4uxUpfs86uoN7h9Ln3y01PC2i1W5i1BmoO9KOkHSi5K+fgjX922u7rfWBoe5ru9cpkKzZg7JQWbpsK0sAFcQyAAAAGCkjDGqKMjUltq2/WPpk5I7lGH5Uj/GmHdK+rKkfZIutdb6D+G2viVLKcaY8cNcV9D3ZY6gRABwHYEMAAAADtfEggzH4/TJR7tUSWxgpozTFQqFJsWSdg8zC+U0Y4wNf/4RSe2SsiRNlVQTebExJit8XpLaIs8DQLwYLpD59ZWLCGQAAAAwrMi+Mik5xfLnJO9rSGbKOPUq1O9lqI/e8HW231hA0qrw+JIhnvf48LHFWts8+mUDwNgLBq2++tDQgcwZc4abLAgAAABIBZk+ZaV6HWOmdLZL1biPUKYfa+211tr0oT4kvSt86fJ+4/dKejI8fukQT31W+Lh6LOsHgLFirdW3//2mHlxJIAMAAIDDZ4xRRaFztowZP8elatxHKDM67lWoke9Jxpjz+58wxpRIujb88OFoFwYAR8paq1v+s1F/eHmnY5xABgAAAIejIqKvjCmdJWvtEFcnNkKZUWCt3Snpz+GH9xljLjfGZBhjFkt6XFKhQjsu3eVSiQBw2G5/Zot+/d+tjrEUj9EvLieQAQAAwMhNjOgrY9JztakmOduv0uh39Fwn6ShJx0j6Y8S5oKSPW2sbol0UAByJO57fph8/uckx5jHSTy45RmcdRSADAACAkcvL8CknPUUtHV3q3rtRqQ3blZ1+httluYKZMiNgrX3OWmuste8Y5FyjpJMl/Vih3Zj6bJB0rrX2L9GpEgBGxx9f2anvPfL2gPFbLjxa5y8sc6EiAAAAJIr3LixT872fVc2fvqzgun+rPD/j4DclIGbKjCJrbbukG40x35Q0R6Hdlja7XBYAjNhfV+3R1//xxoDx714wTxcvmehCRQAAAEgkxdlpkr/b7TJcRygzBsLhzKqDXggAMejhdXv1pb+uHTD+tXPn6MoTpkS/IAAAACBBsXwJALDfU2/V6Po/r1Ewovn99e+cqY+fOt2dogAAAIAERSgDAJAkvbhlnz5932r5IxKZT5w6TZ87c6ZLVQEAAACJi+VLAJCElixZourq6gMDRVPkfefnZXzpjuuCG5/VL/74Cf0iyvXFmqqqKklSe3v7Qa4EAAAADh2hDAAkoerqalVWVkqSfMWTNf4d1w0IZNrWP6n6R38myQ7yDMnJRq7rAgAAAI4AoQwAJLGUggkqvfT/5MnIcYz3bHtN/hUPKK94nEuVxZbmfTVulwAAAIAERCgDAEnKm1UQCmSy8h3jk4sydf7pH5L32svdKSwG3XjOXNlg0O0yAAAAkGAIZQAgGaVmatwHvyJvrnMmzIS8dJ23YIK8HuNSYQAAAEDyIJQBgCTT0eOX9/TrlFIy1TFenJ2qCxaWyedlYz4AAAAgGnjlDQBJpNsf0CfuXSVTMt0xnpfh0/uOKVeaz+tSZQAAAEDyIZQBgCQRCFrd8MBaPb95n2M8K82rDxxbrqw0Jk8CAAAA0UQoAwBJwFqrr/9jvR5ZX+UYD3a16f3HlCs3w+dSZQAAAEDyIpQBgCRwy3826v7XdjvGgt0dan/8pyrKTnOpKgAAACC5MVcdABLcr57bql//d6tjzAZ6VffQ95TWUeNSVQAAAACYKQMACey+V3fplv9scIx5jBR8/nfq2rXOpaoAAAAASIQyAJCw/r12r/73H+sHjN9y4dGye9a6UBEAAACA/ghlACABPbexVjc8uEbWOse/8Z6jdPGSie4UBQAAAMCBUAYAEsyqnQ361B9XqzfgTGQ+e8YMffTkqS5VBQAAACASoQwAJJC39rbow3etUGdvwDF+1QmT9fmzZrlUFQAAAIDBEMoAQILYVtemq+58Va1dfsf4BceU6Vvnz5MxxqXKAAAAAAyGUAYAEsDepk5d+fvXtK+txzF+xpxxuvXihfJ4CGQAAACAWEMoAwBxrr6tW1f8/lVVNnU6xo+bWqhfXr5IPi8/6gEAAIBYxCt1AIhjLV29uvqu17Strt0xvqA8T3dcvUTpPq9LlQEAAAA4GEIZAIhTnT0BfezulXqjssUxPmNctv5wzTLlpPtcqgwAAADAoSCUAYA41OMP6tN/WqXXdjQ4xsvzM3TvR5epMCvVpcoAAAAAHCpCGQCIM4Gg1Y1/WatnN9Y5xouz0/Snjx2nCXkZLlUGAAAAYCQIZQAgjlhr9Y1/vqF/r93rGM9NT9G9H12mKcVZLlUGAAAAYKQIZQAgjvzg8Y2679VdjrEMn1d3fWSZ5k7IdakqAAAAAIeDUAYA4sSvntuqXz231TGW6vXot1ct1uLJBS5VBQD/v737jrOrrhP//3rPTGbSAyGEUKQHAlJEutJEkGLBggqICiiiu/sTrGBBxfIVZVkR19VVERcEF1xFpNiwRelNRQm9hZCEJJCeTKa8f3+cM2aS3ClJZubOnXk9H4/7+NxzPp9zzntubu6c+55PkSRJG8qkjCTVgKvufJqv/PKhNfbVBVx68ss4dOrmVYpKkiRJ0sYwKSNJg9zP//ocn/7Z39fZf+Fb9uLYPbasQkSSJEmS+oJJGUkaxH7/0PN8+Jq/kLnm/vNftztv2+8l1QlKkiRJUp8wKSNJg9SfH53PWT+8l9b2NTMyH3z1VN5zyA5VikqSJElSXzEpI0mD0O2PL+C9V9zNqtb2Nfaf9ort+dBRU6sUlSRJkqS+ZFJGkgaZu596gff8z92sbFkzIfPml2/NZ163OxFRpcgkSZIk9SWTMpI0iNz3zIucfvndLF/Vtsb+1++9FReduDd1dSZkJEmSpKHCpIwkDRJ/e3Yh7/7+XSxtbl1j/3F7TOE/3rY39SZkJEmSpCHFpIwkDQL/eG4R77zsLpasXDMhc9RuW/D1k/ZhRL0f15IkSdJQ412+JFXZQ3MWc+r37mTRipY19h+x6+Z88x370NjgR7UkSZI0FHmnL0lV9OjcJbzju3fy4vI1EzKHTp3Et0/dl6aG+ipFJkmSJKm/mZSRpCp5Yt5STvnenSxYtmqN/QftOJHvvHM/Ro4wISNJkiQNZSZlJKkKHp+3lJO/ewfzljSvsX//7Tflsnfvz6hGEzKSJEnSUNdQ7QAkabh5ZO4STvnuncxfumZCZp9tN+Hy0w9gTJMfzZIkSdJw4J2/JA2gB59bzKmX3ckLaw1Z2mubCfzPGQcw1oSMJEmSNGx49y9JA+SBZxdx6mXrrrK090s24YrTD2D8yBFVikySJElSNZiUkaQBcO/TL3La5XexZGXrGvv33W5TfnD6/owzISNJkiQNOyZlJKmf/enRebzvintZ0dK2xv4Dd5jI90/b3zlkJEmSpGHKbwKS1I9+8cBsPvi/99PSlmvsP2TnSXz3Xfu5ypIkSZI0jJmUkaR+cu3dMznvp3+jfc18DK/adXO+deq+jBxhQkaSJEkazkzKSFI/+O70J/jSzTPW2f/6vbfi4rfuTWNDXRWikiRJkjSYmJSRpD7U3p588aYZfP/WJ9epe8eB2/L5E/agvi6qEJkkSZKkwcakjCT1kZUtbXzk2r9y0wOz16n7lyN24mPH7EqECRlJkiRJBZMyktQHFi1v4cwr7+GuJ19Yp+6846bx/sN3qkJUkiRJkgYzkzLSILfffvsxZ86caodRU6ZMmcI999wzYNeb+cJyTv/B3Tz2/NI19jfUBV89cS/e/PJtBiwWSZIkSbXDpIw0yM2ZM4dZs2ZVOwx14e6nXuCsK+/lhWWr1tg/tqmBb5+6L4dMnVSlyCRJkiQNdiZlpBoRdXWMn7h5tcMY1Ba/MI9sbx+w611790w+9bMHaGlbc83rLcY3cflpB7D7VuMHLBZJkiRJtcekjFQjxk/cnM9ePb3aYQxqF5xyGIvmz+3367S1J1++eQbf+/O6KyxNnTyWH5xxAFtvMqrf45AkSZJU20zKSNJ6eHHZKs6+5i9Mf2TeOnWv2nVzLj15H8aNHFGFyCRJkiTVGpMyktRLf525kH+56j5mLVyxTt2Zh+7AecftRn2dS15LkiRJ6h2TMpLUg8zkh3c+wxdueJBVbWvOWTOiPvjSG/fkbfu/pErRSZIkSapVJmUkqRvLmlv59M/+znX3r7sC1qSxjfzXO/blgB0mViEySZIkSbXOpEwvRUQdUJeZrdWORdLAuP+ZF/nQNX/hqQXL16k7YPuJfOOUfdhi/MgqRCZJkiRpKKirdgCDWUQcGBE/i4gFQBvQEhGPR8T5EVHxm1hEjIuIC8t2zRHxVERcEBFNAxu9pA3V2tbO1295lBO/fXvFhMz7DtuRq8480ISMJEmSpI1iT5kuRMS7gcuAemA5cAcwGdgR+DxwbEQcmZnNnY7ZFPgjsGe5K4HtgM8Ah0TEMfa0kQa3ZxYs50PX/oV7n35xnbpxTQ1c9Na9OHaPLasQmSRJkqShxp4yFUTE3sB3KBIyFwNbZObBwM7Av5XNXgF8bK1Dr6FIyKwAzgBGAi8BfgMcCXyk34OXtEFa29r57vQnOOaS6RUTMvtsuwk3fvAQEzKSJEmS+oxJmcq+CjQCX83Mj2bmUoAsfBO4vmx3cscBEfFa4Ohy88zMvDwzV2Xms8BbgReBz0XEZgP2U0jqlb/PWsSb/us2vnTzDFa0tK1RV18XnHPUVH581sFst9mYKkUoSZIkaSgyKbOWiBhDMVzpbophSpXcXZZbddr3/rJ8IDOv6tw4MxcB36PoOXN830UraWMsa27lyzfP4IRv3soDsxatU7/dZqP58fsP5pyjdqGh3o9LSZIkSX3LOWXWkpnLgDf10KwjGbOs075Dy/LaLo65mWK403HAlRscoKSN1t6e/N99z/Lvv3qY55c0V2xz6kHb8onjdmNMkx+TkiRJkvqH3zbWU0TUAyeUm78t920OTCj33d7FoX8ry2nreb1temgyZX3OJw13dzyxgC/c+CD/eG5xxfqdJ4/lwjfvyX7bTxzgyCRJkiQNNyZl1t8ZwNbl80vKsvM8MY9UOigzX4iIVmD79bzezPVsL6mCB55dxNd/+wi3zHi+Yn1jfR3/8qqd+MARO9HUUD/A0UmSJEkajkzKrIeI2BK4sNy8PDPvL583dWq27rItqy0EJkVEU+eltCX1n7/MXMilv32U3z1UORkDcNgum/OZ1+3GzpPHDWBkkiRJkoY7kzK9FBF1wBXAROBp4MOdqjsv17K8m9OsKstRQG+TMi/poX4KqycelgRAMP2ReXzvz08y/ZF5XbaaOnksn3rtbhyx6+QBjE2SJEmSCiZleu8LwFEUyZS3Z+bCTnUryrI1M9u7OUdH3WiKXjM9KpfU7lJE9OY00vAwYhTj9n099QecwLu+f1eXzSaOaeRDR03l5AO2dVUlSZIkSVVjUqYXIuLtwCfLzQ9k5p1rNekYstQQEVtk5twuTrVpxyn7OkZpuMpMnn1xBQ/PXcKEk/+dGDGyy7YTxzRy5qE78s6Dt2OsqypJkiRJqjK/lfQgIg4GLi83v5KZl6/dppzEdxkwBtgBWCcpExFjynqApf0UrjQsZCbzljTz8NwlPDJ3KUubWwG6TMhMGtvI+w7bkVMP2o7RjX7sSZIkSRoc/HbSjYh4KXADxRwwPwY+0U3ze4HDgP2AOyrUH1SWizNzUV/GKQ0Hza1tzHxhBU8tWMZTC5axrLmtx2N22WIs7zx4e058+TaManRFJUmSJEmDi0mZLkTEHsAtFMtd/xF4Z2ZmN4f8hiIpcxLwnxXqjy7L+/oyTmmoWtbcyuxFK5m9aAWzF61k7uKVtHf3P7CU7W3kzL9wzRf/lYN2nOi8S5IkSZIGLZMyFUTEFhQJmS2AB4ATerGE9ZXABcArI+L1mXlDp/NtDpxZbt7YDyFLNau1rZ2FK1pYsHQVC5Y1s2DpKuYvbWbxytb1Os+ksY3M/N1VLLjnRqaMH8nBO32mnyKWJEmSpL5hUqayj1IkZAB2A+Z289f212Tm9Mx8OiL+FzgFuDoi3g/8FNgd+C7FUtoLWT0/jTRktbUnLW3ttLS1s6q1neWr2spHK8tXtbFkZSuLV7awaEULy1f1PAypKxNGjWDq5LHsOmUck8Y2ccHXf0nbkgUwfus+/GkkSZIkqX+YlKnsZZ2eN9D969R5Pd1/o0jCvAz44Vrt2oH3ZeYLfRCf1Kda29ppbu14tNHcUjxvaW+npbWdlrbVSZbePO/NMKMNURew9aaj2H6zMeyw2Rg2GT3C4UmSJEmSapZJmQoy8+ieW1U87sWIOAT4PHAWq1dbegg4JzN/1UchSj1qz2TpylYWrmhhaXNr8VjZyrLmordKc2vbPxMxbf2VRdlII+qDKRNGsuWEUWxVlo0NdT0fKEmSJEk1wKRMH8vMZcBHIuIzwDSK1ZYerXJYGsIyk4UrWpi3pJl5S5p5YdkqFq4ohgYN1mRLJWObGthsbCOTxjQV5diirLMnjCRJkqQhyqRMPymTM/dWOw4NPStb2nhu4QpmLSxWJZq/tJmWtsGdfGlqqGN0Yz2jGxsY3VjPmKYGJowawfiRDYwfNYLxI0fYA0aSJEnSsGNSRhrs6kcwcsd9GbX7ofzwzqdZsHRVv15uRH3Q1FBPY0MdI+qDEfV15WP9nzfW19FQX0d9nb1dJEmSJGltJmWkQej5xSv5zYy5/G7G89S/9T/YoqERYIMSMo31dYwb1cDYptWP0Y31jBxRT1NDHU0dZUMdTQ31JlAkSZIkaYCYlJEGiYXLV/GLv8/h5395jjueXECWI5KiTMj0ZHRjPZuPa2LS2CY2HT2CTUY3ssmoEYxurHeFIkmSJEkahEzKSFXU2tbO7x+exzV3z+SPjzzf67lh6uuCLcePZKtNRzFl/Egmj2tiTJP/nSVJkiSplvgtTqqC2YtW8L93zeSau2cyZ/HKHttnexttzz/Boa84iG02HcXk8U001DkxriRJkiTVMpMy0gDJTO59+kW+M/0Jbpkxl55Wqx7b1MCR0yZz3Te/wHP33sK4saM54B3TByZYSZIkSVK/Mykj9bO29uQ3D87hv6c/wf3PLOy2bWNDHUfuOpk3vGwrjpw2mZEj6vnpx+6ifeUSGDt6YAKWJEmSJA0IkzJSP2lpa+e6+2bxX394jKcWLO+27d7bTODkA7bl+L22ZPzIEQMUoSRJkiSpmkzKSH2sta2d6+6fxTd+9xjPvNB1MmZMYz0n7LM1pxywLXtsPWEAI5QkSZIkDQYmZaQ+0taeXP+XWVz620e77Rmz9SajOP2V23PSAdsy1hWTJEmSJGnY8huh1AemPzKPL//iIWbMXtxlm923HM9Zh+/I8XtuyYh6V06SJEmSpOHOpIy0ER58bjFf/sUM/vTo/C7bvOwlm3DOUVM5fJfNiYgBjE6SJEmSNJiZlJE2wPylzVz0y4e59t6ZZBdLW++9zQTOOXoXjjAZI0mSJEmqwKSMtB5a2tq58van+dotj7BkZWvFNrtsMZaPHzONV+822WSMJEmSJKlLJmWkXrrtsfl87oZ/8MjcpRXrJ49r4iOv2YW3vHwbGpwzRpIkSZLUA5MyUg8WLG3mizfN4Lr7Z1WsH91YzwcO34n3HLoDoxv9LyVJkiRJ6h2/QUpdyEx+ct8svnjTgyxc3lKxzZv22ZrzjpvGFuNHDnB0kiRJkqRaZ1JGquDJ+cv41HUPcNvjCyrWv3Sr8Vzwhpey3/YTBzgySZIkSdJQYVJG6qSlrZ3vTH+Cr//2UVa1tq9TP25kA+ceO42TD9iW+jon8ZUkSZIkbTiTMlLpvmde5BM/eYCH5y6pWP/avbbks6/bnckOVZIkSZIk9QGTMhr2lja38tVfPsSVdzxN5rr1W00YyRfeuAev3m2LgQ9OkiRJkjRkmZTRsHbrY/P5+P/9jVkLV6xTVxdw+it34MNH78KYJv+rSJIkSZL6lt80NSwtbW7lyzfP4Ko7n6lYv/uW47nwLXuy1zabDGxgkiRJkqRhw6SMhp3bHpvPx7roHTNyRB0fPnoXznjlDjTU11UhOkmSJEnScGFSRsPGsuZWvvyLGfzwjsq9Yw7cYSIXnbg32242eoAjkyRJkiQNRyZlNCzc9ngxd8yzL67bO2bUiHo+cfw0Tj1wO+pc5lqSJEmSNEBMymhIW9bcyoW/KFZWqsTeMZIkSZKkajEpoyHrtsfnc+5P/sbMFyr3jjnvuGm88yB7xwwlSxcuAGD27Nlss802VY5mcJs9ezYAy5Ytq3IkkiRJ0vBlUkZDTk+9Yw7YYSIXnbgX2202ZoAjU39rb2//Zzlr1qwqR1Mbsj2rHYIkSZI0bJmU0ZBy++ML+PhP/lqxd8zIEXWce+w03n3w9vaOGfKCCZMmVzuIQW3R/LnVDkGSJEka9kzKaEhY2tzKhd2srHTA9hP56ol7sf0ke8cMB/VNo/ns1dOrHcag9pFjdyPLnkWSJEmSqsOkjGre9Efm8YmfPsCshfaOkSRJkiTVDpMyqlmLV7bwpRtncM09MyvW77/9plx04t72jpEkSZIkDUomZVSTfvfQXD75078zZ/HKdepGjajnY8fsymmvsHeMJEmSJGnwMimjmrJw+So+f8OD/PT+yivrHLTjRL7yFldWkiRJkiQNfiZlVDN++fc5nH/935m3pHmdujGN9Xzi+N045YBt7R0jSZIkSaoJJmU06M1auILPXv8PbplReQnfQ6dO4stv3pNtNh09wJFJkiRJkrThTMpo0Gpta+fyW5/ia7c8wvJVbevUjxvZwPmv3Z237rcNEfaOkSRJkiTVFpMyGpTuf+ZFPnnd35kxe3HF+iOnTeb/vWlPpkwYOcCRSZIkSZLUN0zKaFBZtKKFi371EFfd+QyZ69ZPGtvI+a/bnTfsvZW9YyRJkiRJNc2kjAaFzOTnf32OL9w4g/lL153IF+CUA7fl3GOmMWH0iAGOTpIkSZKkvmdSRlX32PNLuOCGB/nTo/Mr1k+bMo4vvWlP9t1u0wGOTJIkSZKk/mNSRlWzaHkLl/z2Ea64/Wna2tcdqzRqRD3nHDWVMw7ZgRH1dVWIUJIkSZKk/mNSRgOurT350V3PcPGvH+bF5S0V27x62mQuOOGlLnMtSZIkSRqyTMpoQN3xxAI+9/N/8NCcJRXrp4wfyefesDvHvHSKE/lKkiRJkoY0kzIaULc9vqBiQqaxoY6zDtuR9x++E2OafFtKkiRJkoY+v/1qQL3/8B358T0zmb1o5T/3HbfHFD55/G68ZKJDlSRJkiRJw4ezp2pAjW5s4LzjpgHFqkpXv/dAvnXqviZkJEmSJEnDjj1lNODesPdW1NcFx750Cg2uqiRJkiRJGqZMymjARQSv22uraochSZIkSVJV2U1BkiRJkiSpCkzKSJIkSZIkVYFJGUmSJEmSpCowKSNJkiRJklQFJmUkSZIkSZKqwKSMJEmSJElSFZiUkSRJkiRJqgKTMpIkSZIkSVVgUkaSJEmSJKkKTMpIkiRJkiRVgUkZSZIkSZKkKjAp0w8iYkREnBcRD0ZEc0Q8FxFfj4gJ1Y5NkiRJkiQNDg3VDmCoiYgm4EbgqHJXAlsCHwSOjIhXZubiasUnSZIkSZIGB3vK9L1vUCRk2oGPA+OAScAPgT2Ar1QvNEmSJEmSNFjYU6YPRcSewHvKzc9k5kXl82URcQawP3BWRHwzM/9elSAHif322485c+ZUO4yaMHv2bACWLVtW5UgkSZIkSX3JpEzfeh9F76P5wEWdKzKzJSIuBb4JvAkY1kmZOXPmMGvWrGqHUVOyPasdgiRJkiSpD5mU6VuHleX1mbmqQv3NZXkc8IWBCWlwi7o6xk/cvNphDGqL5s+tdgiSJEmSpH5gUqZv7VyWt1eqzMynImIxMK23J4yIbXposnXHk45hLrWgra0NgLETJnL216+tcjSD2+dPPQIyyWxn4TyHfHUnM8vS16onvlbrx9er93ytes/Xqvd8rXrP16r3fK3Wj69X7/la9V57+b2wra2NZ599tsrR9Gyt79z1fXHO6HjDaONExEhgRbl5WGb+qYt2jwE7ARN6swpTRPgPJEmSJEnS4LJ/Zt6zsSdx9aW+09Tp+YvdtOuo27IfY5EkSZIkSYOcw5f6Tlun58u7adcx18yoXp73JT3UN1IMh3oemLdWHIPVFODu8vn+gH361Bd8X6m/+N5Sf/B9pf7g+0r9xfeW+kMtvq/qgY5JUR/oixOalOk7Kzo9rzTJb4f2shzdm5NmZm8G1j3Rm3MNFhHReXNOL39GqVu+r9RffG+pP/i+Un/wfaX+4ntL/aGG31dP9+XJHL7URzKzDVhSbm7dTdNNyzK6aSNJkiRJkoY4kzJ965my3KGbNluU5dJ+jkWSJEmSJA1iJmX61r1luV+lyojYCZhUbj5TqY0kSZIkSRoeTMr0rd+U5YkRUWnN8qPL8qnM7G6FJkmSJEmSNMSZlOlb1wGLge2Af+lcEREjgQ+XmzcOcFySJEmSJGmQMSnThzJzGfCf5eZ/RMTZETEuInalSMRMBVqBb1QrRkmSJEmSNDiYlOl7nwd+TbHc+CUUPWceAl5d1p+XmY9UJzRJkiRJkjRYRGZWO4YhJyIagHMphitNLHc/S5GQuapqgUmSJEmSpEHDpEw/iogmYDdgFTAjfbElSZIkSVLJpIwkSZIkSVIVOKeMJEmSJElSFZiUkSRJkiRJqgKTMpIkSZIkSVVgUkaSJEmSJKkKTMpIkiRJkiRVgUkZSZIkSZKkKjApI2lIiYi6iGiodhySJEmSNkxENETEsMhXDIsfUtLQFhEHRsTPImIB0Aa0RMTjEXF+RIysdnwaOsr32qqI+EO1Y1Hti8KvIyIjYvpwuflU/4iIV0fEzRExr/ycejYi/hARZ0REfbXjkzR8RcS3yt912/fQ7uSIuDUilgEtwKqIuDsiTh6QQKvEX/4aUBExIiLOi4gHI6I5Ip6LiK9HxIRqx6baFBHvBm4FTgBGAncATwA7Ap8HfhsRTdWLUENF+Tn1I2BEtWPRkPGvwNHAMuD0zGyvcjyqURHxAeA3wHHAJKAe2Bo4HLgM+EVEjKpehKoV6/HleYuy7czynv6hiDjH5LLWFhHnAu/vRbvvAlcDr6D4vXgr8AKwH3B1RPx7f8ZZTf6n0YApvxjfDHwZ2I3ii82WwAeBP0fE+CqGpxoUEXsD36G4+bwY2CIzDwZ2Bv6tbPYK4GPViVBDzHeAHaodhIaGiNgF+Eq5+fHMfLya8ah2RcSuwKUUf1U+E5hIcY+1LXAe0EqR/PtItWJUbViPL8/bAfeUbbeheL/tCnwNuLw/Y1RtiYgPU3z366ndB4D3AsuBMyju6Q8BtgOuKJt9JCIO6a9Yq8mkjAbSN4CjgHbg48A4ir/m/BDYg9U3p1JvfRVoBL6amR/NzKUAWfgmcH3Zbkh3eVT/i4gzgbcBWe1YVPvKoSRXAKOBW4BvVTci1biTgAbgvzPze5n5Yma2Z+bMzPwKxf0XwOurF6IGu/X48jyC4o+s2wALgDdSJGWmAX8B3hURb++3QFUTImJ0RPyI4o+mz/bQdgzwxXLzxMy8PDMTIDNXUCRr5pX1Q/Ke3qSMBkRE7Am8p9z8TGZelJnLMnMBRTb0YeCsiNijakGqppQf4MuBuymGKVVyd1luNSBBaUiKiN2BSygSMhdXNxoNEZ8ADgQWA+/puPmUNtDWZfloF/XPlKXDl7SO9fnyXHofsDvF78Q3Zeb1mdmWmQ8Db6HomXWJ8xgNe5+jSBjfCRzQQ9tpwL3AlZn5i7UrM7MF+Gu5OSTv6U3KaKC8j+L9Nh+4qHNF+R/tUiCANw18aKpFZVLvTZl5QGYu66JZxwd3V/VSt8qJoq+h6NHw78BN1Y1ItS4i9gE+U26ek5nPdNde6oVZZXl8F/WvK8t7ByAW1Z7P0fsvz7B6eNONmfmnzhWZ+QRwHTCFYvi4hq924HzgkMyc013DzLw3M1+Tme/qptmQvqc3KaOBclhZXp+ZqyrU31yWxw1QPBriyr/QnFBu/raasaimXUIxvPI24JPVDUW1LiIaKYYtjQCeBCZGxCURcVFEvLWsl9bXVRRfVI4tJ16dGhGjImJaRFwOvBpYAfxHVaPUYNXrL88RsSnF70SAa7to5j29AM7PzC9mZuvGnigiplH0zoIhek/fUO0ANGzsXJa3V6rMzKciYjFF9zWpL5zB6i7dl1QxDtWoiDgROItizPzbM7M1IqoclWrcR1n9hWZLirkYlgN7lXWPRMQpmWmPBvVaZj4WEccB/0PRi2HtiVpnUAyTe2DAg1MtOL/std4bO3d6XvGeHvhbWXpPP4ytx3uqN84vyznA//bheQcNe8qo35Xd/0eXm49003QesKmrMGljRcSWwIXl5uWZeX8141HtKVeW+C7FmPl3ZWZvxtlLXYqIKazubXU7sENmHpqZx1AkkP8V2BH4fUS8tEphqnaNB0Z2UbecIgkorWM9vzxvVpYtwFNdtHm+LLffwJCkf4qIY4BTys1zy4l/hxyTMhoITZ2ev9hNu446bxy0wSKijmJ4wETgaeDD1Y1ItSYiGoAfAZtQrOx1c/dHSL3yL8AYikkw3955mEC5Us5/UaySMw64oDohqhZFxBEUqw123D89CUxn9aSt+wI/iYiPD3hwGmo67ukXZ2ZbF228n1efiIgtgB+Um9dn5hXdNK9pJmU0EDp/aC/vpl3HXDOuDqCN8QWKpdebKb74LKxuOKpBXwAOBm4FPl3lWDR0HFmWN2fmzC7a/LQsX1cmmKXe+BpQDzwHHJGZO2bm4cB2FH9hXli2+0xETKxOiBoiOu7pvZ9Xvyr/QHYNxaTRTwKnVzei/uUvfA2Ezt3MKk3y26G9LEd300bqUkS8ndXDAz6QmXdWMx7Vnog4CjiXYqW4k/pigjqptHlZdve5NLcsm4DJ/RuOhoJyqOXLys13ZuYfO+rKHlg/Ak4ud41h9cIL0obouKf3fl797VLgcGApcEJmdjfaouaZlFG/K7s3Lik3t+6m6aZl6UyaWm8RcTBwebn5lcy8vLv2UhdOpfgMmgTMjIjs/AB+X7Y7vNP+06oVrGrK4rJc2E2bcZ2eD8llP9XnOu6rlrD682kNmflLVt+HOaREG6Pji/FW3bTxfl4bJSLOAT5A0TPrpOEwSblJGQ2UZ8pyh27abFGWS/s5Fg0x5aSYN1B0lf0x8InqRqQa1kIx9K2rR8eEiNlpX1fj6qXO/lGW3a1IclBZzsrMJd20kzp0JO9aMjN70X5I/7VZ/a7jfn5UOXl5Jd7Pa4NFxMnAxeXm2Zl5UzXjGSgmZTRQOpb33K9SZUTsRPGXaVj9gS/1KCL2AH5LsSLAHym6b/fmxlRaR2aemZkju3oArymbTu+0/8pqxqya0XFj+e6IWOevzBExDvhQuXnDgEWlWvcoxeTREyOiYm/kiHg5q3th3TZQgWnoycz5rL5Pr3hPz+rksvfzWi8RcRLFYh11FL3ev1nlkAaMSRkNlN+U5YkRUV+h/uiyfGqojxlU3ylnZb+F4q8yD1CMOW2ublSSVNFPgPspli7+dUS8OiJGRERDRBxJsVrOzsBK4KtVjFM1JDOXA9eWm59cuz4ixgAdX2z+LzP9oqyN1XFPf1IX9R339PcNQCwaIso5/X4INABXZOZ5VQ5pQIV/UNZAKG8KnqO4Gf1gZn6jU91I4G/AVOA/M/P/q06UqjURcRHw0XKzle6HkbwmM6f3f1QaysqlZ38P/DEzj6hqMKo5EbEjxdLFe5S7Om7COuZeaAPelpk/XftYqSsRsRlFj9G9gbspvjQvBbYHTgQmAg8Dh5Q9HaQulfOnAeyQmU9VqD8E+BPFfdeBmXlfp7ppwF+BRuDEzPxJ/0esWtCL99XdrO59tYrVvx/XUfZcHlLsKaMBkZnLgP8sN/8jIs6OiHERsStwI0VCphX4RlfnkCp4WafnDRQrlnT18PNOUlVl5hPAgcD7KJJ7z1H87ltA8bvwcBMyWl+ZuYBiyMgHKVbHOQv4PMWqS08Dn6P48mxCRhstM/8M3Epx33VTRBwfEU0R8SrgFxQJmceBn1cxTNWel3V63kj39/RDjj1lNGAiooniA/o1XTT5aGZe3EWdJEmSpH7UU4+Gss12FEMut61QvRI4KjNv7Z8IpaHHvxxrwJRzfbwW+DTwQqeqZ4FTTchIkiRJ1ZOZUT6e6qbN0xRDTX5AMdSkw13AYSZkpPVjTxlVRdlrZjeKD/IZrpYjSZIk1ZaImEAxDcG8MlkjaT2ZlJEkSZIkSaoChy9JkiRJkiRVgUkZSZIkSZKkKjApI0mSJEmSVAUmZSRJkiRJkqrApIwkSZIkSVIVmJSRJEmSJEmqApMykiRJkiRJVWBSRpIkSZIkqQpMykiSJEmSJFWBSRlJkiRJkqQqMCkjSZIkSZJUBSZlJEmSJEmSqsCkjCRJqjkRsVlEjOhFu4iI90XEqRFxyAZe660RMS0i+v2+KSKOjIg9uqnfOSKuiIi3R8SEcl9ExFkRsV0/xHNiRNwSEd/o63NLkiSTMpIkqcZExCbADcBNETGmu7aZmcB/AVcCb9yAa40CrgZmAGev7/Eb4HvAAxFxQxf1BwPvLGPaqdx3IfBt4Ev9EM+uwKuB95avuyRJ6kMmZSRJUq05AjgAOBr4ZU+JGWBlWT65Adc6BGgAFgDf2YDjey0i9gF2AFqAj3fR7IiyvCkz7yuffw9YBZwcEbv3cVjfB9qBkcDb+/jckiQNew3VDkCSJGl9ZObPIuItwP9SJE2uj4jXZmZzF4c0A2OARRtwuWPL8urMXLYBx6+PjqTH9zNzxtqVEVEPnFBuXtaxPzMfjYjvAR8AXgM82JuLRcQ9wCa9aNpO8Ye8iyLiY2ufBmgEmigSN5tmZltvri9JkiCKXr2SJEm1JSJeD/yMImFwdWa+o4t2zwObAydl5jXreY2ngO2AgzPzjnJfAPUUf9waAdRn5sKIeAjYuRen/URmXrTWdUYAM8s4d8nMxyvE8mrgFuBFYEpmrupUtxUwITNnlHPf1AOjKO71KiajImI2MAWYR9HTZkPUU7wGTeX1RmZm6waeS5KkYceeMpIkqSZl5g0R8SngQ8ClvThkvYZtR8QrKRIyALcXuZiKfkXRo6aZIknxODCnQru9gbHAigp1bwS2AH7akZCJiM8BTwA/KXvpvLtsuynQXCmeCvuuAU7qIu6O5MmbMvPWTueYwOr5aT6bmQvWusZWFPP0AJyWmQu7OL8kSeqBSRlJklSzMvPCiPhRZj7dTbOOVZpGrufpzyrLFyh6p8DqyXWfpBjWA/BcWXbMXfPvmfnttU9WDhfalyJ5s7YPleXFZdtG4CMUSZxJEfED4K1lm1nAY52O3RzYHZgLPMTq4URNwOxufr6OpMza3aYT+Nfy+QUVjktWD6OSJEkbwaSMJEmqaT0kZKBITgCM7+05y94gHXO8vCEzby2HGHUM89krM5eudVhvh+2s0S4iXkuxqtKdmXlbufutFAmZ+4FLgHNZnVT6v8w8p9PxJwI/Bn6Zmaf1MobO1k7KNHfxvEPnoU7LN+B6kiSpZFJGkiQNehExnmJS2eWZ2bIexzVQzHUCMHk9LnkuRY+T2zsN7WnqVF9pDpbeTtT3z3YRsTmrhwJdXMY7hdXLb38YGFeW/aUtIqZT9AJqplj9qcPdEbH2z9V5GNgD5QTEIygmU355Zj7Tj7FKkjSkuCS2JEmqBV8FFgKrIiI7PU7t4bjOvWN26M2FImIi8Ppy8xOdqjp6qrR1nmR3I50NbFs+/yZFUmQmsD9Fz5c/AP8PmESxLHdf6rgPbAdGU7xWY8tHh7EVHp2XIB+31qPLiXckSdK67CkjSZJqwUqKyXObgTaKCXjrWT2PS1de0ul5r5IymfkCsGNEbJmZnedk2aIsF/cq4t75AfBJYBnwDDAdOI4iSfLpiNgPeD/Fz/xt4FPAaRHxuk7n6EiSvDkiDqN4XZqAOZn5sm6u3ZGUacnM/Tp2lvPZdAxb2nXtYVoRsQnlHDuZudV6/KySJGkt9pSRJEmDXmaek5lbZub2mbkTML+s6ikps1On53uUw4O6FRGNETGSdVdQ2rosF3VqGxExpkxkrLfMfAx4OcVy1vtRDGUaDfwsM+8FJlBM4HsN8FR5WD1Fr52OR2On/aM6PXqa2LhjAuS1h4P1+v4wulmSSpIk9cykjCRJqmWVJqLtbM9Oz8cC+3XVsJN3USxb3R4RrRHRHBErgRvL+u3Kfa0UQ3+WAqesZ9z/lJl/ycyOlZy+RDHnzGfKut8Ce1D0kOlwWWZu0/Fg9SpRPy4TV5Mzc0JmTuvh0h3JnLVfw/r1CN9e15IkbQR/kUqSpFrW0+S6x5XlI8AuwBHAHb045wqKXjgtFEOHEtiUonfJUorhRnXl9uhexNGjiDgZOAi4NjMf+GcwxXCqF/qhU0rHBMhLutgPsKSH6zaybk8bSZLUSyZlJEnSkBQRkygmzG2hmLfl/yiWub6wu+My8zLgsgrnu41i6epzM/NbFerP3IhYx1NMZtwOXLCh51mP6zWwenjTi2tVNwAP9/JUTRQJKkmStAFMykiSpCEhIvbPzLs77XoPRW+WPwA3UfRweVlE7FvO17I+565n9VCov/dBuJ3P3QRcDWwD/AiYGRH7ADsDU4H/zsxer7xUxtoEjM7M+V0027Qsl2VmS5mk2ZSid9CLwG6ZudG9fyRJUvecU0aSJNW8cjWi2yLi4HK7EfhgWX1FZq4Efl5un1XhFD15FauXih7bXcMN8BXgteXzEyhWd7oPuBY4B3hhrfZnd14WHPhxuf/d5XYrRe+VZ7u5ZsdKUh2rS+0MPF9eeyXFfDrZy8dXN/gnlyRpmLOnjCRJqmkRcRDF6kQNwJnA7cDpwFYUKyV1JC1+SDEh72kR8eXMfHI9LnNSp+c/i4gPZOb3Nzr4QudeO6OBJ4GnKZIk91XosTKTYo6cDpMpevHMBmZQvA6NdD9h77Zl2fEaNAMPAMsphjXtTdGz6M4ujt+N4vWdyQAMt5IkaagyKSNJkmrZSylWKhoN/An4t4jYktXzxnw9M5eXz38FPAHsSJFIeFdvLhARWwDvKDd/CRwDXBYRu2TmeX3wM9wEnA/cBdydmWvP8bK2n2bmOZ3iO5Ei8fTrzDytl9fcuSyfBCgTVHuV52sE/grsClyamT/vfGBE7ETRk6cNODUznVNGkqQN5PAlSZJUyy4GJlL06HhtmYD5T2ATYD7wtY6G5bLTHUNt3hERB/TyGh+l6D0yi2J40bsphgidGxGVJg3+VqVhPsC+Zf0ayxll5guZ+cXM/HWlhEwU+vqerWNp8AfXrsjMVRTz8bQD/xMRUzvFMh64HhgPfC4zp/dxXJIkDSsmZSRJUk2JiBGsnqi2HrgfODYzl0TE2cCby7qzM3PhWod/n2JloTrgyjLJ0N21prJ6bpoLMnNVZl4JvJUiaXFuRLy/rG8sy7nlNdZ+rFyr3drXGhkRUyPimIg4OyL+OyJupRiCNa27ONdHFGtcv6rcvKtSm8y8Dfg0RXLrDxHx0ogYR9Gr56XAVZn5xb6KSZKk4SqcWF+SJNWScunp75SbTwEHZebciHgj8BOKhMuPMvOULo4/BPhj2e53wPGZ2VyhXR1wC0UC41bg0M7zu0TER4GLKOZ+2ZVilae9gQ9k5rcrnO8eit4yZ2fmpeW+I4FLgC2BSRXCTeBx4BXAG8uf+8Xymh3GUczvsphiXpmgSPx0PG7LzBM6xXEgcAdFsmdSZrZWep3Ktt8F3ksx2fBcirlkrgfeVvaokSRJG8GeMpIkqdb8niIZs4QioTI3Io6nWFa6jmLC2jO7Ojgz/0wxhwvAkcAtEbFZhaafokjILAFOrzDh7sXAFcCBZY+cpl7G37mnzAMUPU86EjKryp/v88CxwMTMnJqZ81g9ce8IihWgOh5JMbRqyVr7x5WPUWtd/31leVN3CZnS94F5FEPEdqOY/PdbFL2EJEnSRnKiX0mSVFMy87GIOAzYLTNnlD1WvkKRkJlNMbdMT5PPfhmYCpwGHAL8NSLekZl/BIiIM1i9qtD7M/PRCnEkxfwyHeooJr/tKmFxJUVvmvs6nWNeRPyBotfK/wC3dBN7R1Lmss4T/a6PiNiKYgUqgKsq1I8DDgSOBl4H7F5WzafogbMZxWTHi8qhVfdTrAT1FMUS3EuBxeUS5JIkqQcmZSRJUs3JzJnAzHJOmL0oEiLzgKPKup6Oz4h4L0XvlpMpEib3dGqyR1lelJlX9zKmXXuo/3oXVa/JzLZeXKK3PXG6cwxFcudp4FcRsQ3F5MebUQzB2pbVExGvAm6m6A10Xbn/zRTz6RwNHF8+1vbu8hhJktQD55SRJEk1LyJOo5g75ZH1PK4OOCEzr6tQdwTwxwrDlqoiIj4FfJFime9zNuI82wK7Z+Yvy+2vAedQJGH+QTF/zh+BX2Xmki7O0QjsT9GrZn9gT4qeR49m5h6VjpEkSesyKSNJkjSMlUOWtqFIqPQ0x0x356kHxlda1luSJFVmUkaSJEmSJKkKXH1JkiRJkiSpCkzKSJIkSZIkVYFJGUmSJEmSpCowKSNJkiRJklQFJmUkSZIkSZKqwKSMJEmSJElSFZiUkSRJkiRJqgKTMpIkSZIkSVVgUkaSJEmSJKkKTMpIkiRJkiRVgUkZSZIkSZKkKjApI0mSJEmSVAUmZSRJkiRJkqrApIwkSZIkSVIVmJSRJEmSJEmqApMykiRJkiRJVWBSRpIkSZIkqQr+f4t0FgpXiEqDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 绘制直方图\n",
    "import seaborn as sns\n",
    "sns.histplot(data=df, x=\"认知维度\",binwidth=1,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述统计\n",
    "## 双变量描述统计\n",
    "### 定类与定类\n",
    "result = pd.crosstab(\n",
    "        df['会打多少分'],\n",
    "        df['政治面貌'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>政治面貌</th>\n",
       "      <th>党员</th>\n",
       "      <th>其他</th>\n",
       "      <th>团员</th>\n",
       "      <th>群众</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会打多少分</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20~40</th>\n",
       "      <td>1.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>八十到一百</th>\n",
       "      <td>24.53</td>\n",
       "      <td>22.22</td>\n",
       "      <td>27.93</td>\n",
       "      <td>20.21</td>\n",
       "      <td>26.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六十到八十</th>\n",
       "      <td>42.45</td>\n",
       "      <td>44.44</td>\n",
       "      <td>49.92</td>\n",
       "      <td>46.81</td>\n",
       "      <td>48.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四十到六十</th>\n",
       "      <td>26.42</td>\n",
       "      <td>33.33</td>\n",
       "      <td>15.87</td>\n",
       "      <td>25.53</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零到二十</th>\n",
       "      <td>4.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.81</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "政治面貌      党员     其他     团员     群众     合计\n",
       "会打多少分                                   \n",
       "20~40   1.89   0.00   3.47   4.26   3.28\n",
       "八十到一百  24.53  22.22  27.93  20.21  26.49\n",
       "六十到八十  42.45  44.44  49.92  46.81  48.48\n",
       "四十到六十  26.42  33.33  15.87  25.53  18.71\n",
       "零到二十    4.72   0.00   2.81   3.19   3.04"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为：0.006，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tau_y = mytools.goodmanKruska_tau_y(df, '政治面貌', '会打多少分')\n",
    "F'tau_y值为：{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['零到二十', '20~40', '四十到六十', '六十到八十', '八十到一百'])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.variable_value_labels['会打多少分'].values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>年级</th>\n",
       "      <th>预科</th>\n",
       "      <th>大一</th>\n",
       "      <th>大二</th>\n",
       "      <th>大三</th>\n",
       "      <th>大四</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>会打多少分</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>零到二十</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4.17</td>\n",
       "      <td>4.19</td>\n",
       "      <td>2.15</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20~40</th>\n",
       "      <td>0.00</td>\n",
       "      <td>3.16</td>\n",
       "      <td>4.92</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.15</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四十到六十</th>\n",
       "      <td>35.71</td>\n",
       "      <td>12.98</td>\n",
       "      <td>23.86</td>\n",
       "      <td>19.76</td>\n",
       "      <td>17.20</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六十到八十</th>\n",
       "      <td>50.00</td>\n",
       "      <td>53.33</td>\n",
       "      <td>42.05</td>\n",
       "      <td>51.50</td>\n",
       "      <td>46.24</td>\n",
       "      <td>48.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>八十到一百</th>\n",
       "      <td>14.29</td>\n",
       "      <td>28.77</td>\n",
       "      <td>25.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>32.26</td>\n",
       "      <td>26.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "年级        预科     大一     大二     大三     大四     合计\n",
       "会打多少分                                          \n",
       "零到二十    0.00   1.75   4.17   4.19   2.15   3.04\n",
       "20~40   0.00   3.16   4.92   1.80   2.15   3.28\n",
       "四十到六十  35.71  12.98  23.86  19.76  17.20  18.71\n",
       "六十到八十  50.00  53.33  42.05  51.50  46.24  48.48\n",
       "八十到一百  14.29  28.77  25.00  22.75  32.26  26.49"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 定序与定序\n",
    "### 一定要注意参与运算的数据必须为定序变量\n",
    "\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['零到二十','20~40', '四十到六十','六十到八十', '八十到一百'], ordered=True)\n",
    "df = df.astype({'会打多少分':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['会打多少分'],\n",
    "        df['年级'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.027250783174111757"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "# 取得各个定序变量\n",
    "x = df['年级'].cat.codes\n",
    "y = df['会打多少分'].cat.codes\n",
    "dy = stats.somersd(x, y)\n",
    "# 打印萨莫司dy的值\n",
    "dy.statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'萨莫司dy值为：-0.027，该值属于极弱相关或不相关，p值为0.323，接收虚无假设，拒绝研究假设。'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = dy.pvalue\n",
    "f\"萨莫司dy值为：{dy.statistic:.3f}，该值属于{mytools.draw_on_corr(dy.statistic)}，p值为{p:.3f}，{mytools.p_result(p)['conclusion']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 构造另一个定距变量\n",
    "df['情感维度']= df['您认为中国有多少值得自豪的地方'].cat.codes + df['会以中国人自豪吗'].cat.codes + df['国歌升起'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 定距与定距\n",
    "mytools.gen_scatter(df,'情感维度','认知维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'积矩相关系数r为：-0.008，决定系数r平方为：0.000，相关强度为极弱相关或不相关。'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.pearsonr(df['情感维度'], df['认知维度'])\n",
    "f\"积矩相关系数r为：{r:.3f}，决定系数r平方为：{r*r:.3f}，相关强度为{mytools.draw_on_r(r*r)}。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 定类与定距\n",
    "\n",
    "sns.boxplot(\n",
    "        x='政治面貌',\n",
    "        y='认知维度',\n",
    "        data=df,\n",
    "        color='white',\n",
    "        linewidth=1,\n",
    "        width=0.5,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'相关比率为：0.009，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为极弱相关或不相关。'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算相关比率\n",
    "from statsmodels.formula.api import ols\n",
    "model = ols('认知维度 ~ 政治面貌', df).fit()\n",
    "eta_2 = model.rsquared\n",
    "f\"\"\"相关比率为：{eta_2:.3f}，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为{mytools.draw_on_eta2(eta_2)}。\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytools.to_sav(df_clean,metadata,'./data/indentity问卷数据数据清理后.sav')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9650cb4e16cdd4a8e8e2d128bf38d875813998db22a3c986335f89e0cb4d7bb2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
